Information Theory For Data
Management
Divesh Srivastava
Suresh Venkatasubramanian
Motivation
-- Abstruse Goose (177)
Information Theory is relevant to all of humanity...
Background
 Many problems in data management need precise reasoning
about information content, transfer and loss
Structure Extraction
– Privacy preservation
– Schema design
– Probabilistic data ?
–
Information Theory
 First developed by Shannon as a way of quantifying capacity
of signal channels.
 Entropy, relative entropy and mutual information capture
intrinsic informational aspects of a signal
 Today:
Information theory provides a domain-independent way to
reason about structure in data
– More information = interesting structure
– Less information linkage = decoupling of structures
–
Tutorial Thesis
Information theory provides a mathematical framework for the
quantification of information content, linkage and loss.
This framework can be used in the design of data management
strategies that rely on probing the structure of information in
data.
Tutorial Goals




Introduce information-theoretic concepts to VLDB audience
Give a ‘data-centric’ perspective on information theory
Connect these to applications in data management
Describe underlying computational primitives
Illuminate when and how information theory might be of use in
new areas of data management.
Outline
Part 1
 Introduction to Information Theory
 Application: Data Anonymization
 Application: Data Integration
Part 2




7
Review of Information Theory Basics
Application: Database Design
Computing Information Theoretic Primitives
Open Problems
Histograms And Discrete Distributions
X
x1
x3
x2
x4
x1
x1
x2
aggregate
counts
x1
Column of data
X
f(X)
X
p(X)
x1
4
x1
0.5
x2
2
x2
0.25
x3
1
x3
0.125
x4
1
x4
0.125
Histogram
normalize
Probability
distribution
Histograms And Discrete Distributions
reweight
X
x1
x3
X
f(x)*w(X)
x1
4*5=20
x2
2*3=6
x3
1*2=2
x4
1*2=2
normalize
x2
x4
x1
x1
x2
aggregate
counts
x1
Column of data
X
f(X)
X
p(X)
x1
4
x1
0.667
x2
2
x2
0.2
x3
1
x3
0.067
x4
1
x4
0.067
Histogram
Probability
distribution
From Columns To Random Variables
 We can think of a column of data as “represented” by a
random variable:
X is a random variable
– p(X) is the column of probabilities p(X = x1), p(X = x2), and so on
– Also known (in unweighted case) as the empirical distribution
induced by the column X.
–
 Notation:
X (upper case) denotes a random variable (column)
– x (lower case) denotes a value taken by X (field in a tuple)
– p(x) is the probability p(X = x)
–
Joint Distributions
 Discrete distribution: probability p(X,Y,Z)
X
Y
Z
p(X,Y,Z)
X
Y
p(X,Y)
X
p(X)
x1
y1
z1
0.125
x1
y1
0.25
x1
0.5
x1
y2
z2
0.125
x1
y2
0.25
x2
0.25
x1
y1
z2
0.125
x2
y3
0.25
x3
0.125
x1
y2
z1
0.125
x3
y3
0.125
x4
0.125
x2
y3
z3
0.125
x4
y3
0.125
x2
y3
z4
0.125
Y
p(Y)
x3
y3
z5
0.125
y1
0.25
x4
y3
z6
0.125
y2
0.25
y3
0.5
 p(Y) = ∑x p(X=x,Y) = ∑x ∑z p(X=x,Y,Z=z)
11
Entropy Of A Column
 Let h(x) = log2 1/p(x)
 h(X) is column of h(x) values.
H(X) = EX[h(x)] = SX p(x) log2 1/p(x)
X
p(X)
h(X)
x1
0.5
1
x2
0.25
2
x3
0.125
3
x4
0.125
3
H(X) = 1.75 < log |X| = 2
Two views of entropy
 It captures uncertainty in data: high entropy, more
unpredictability
 It captures information content: higher entropy, more
information.
Examples
 X uniform over [1, ..., 4]. H(X) = 2
 Y is 1 with probability 0.5, in [2,3,4] uniformly.
H(Y) = 0.5 log 2 + 0.5 log 6 ~= 1.8 < 2
– Y is more sharply defined, and so has less uncertainty.
–
 Z uniform over [1, ..., 8]. H(Z) = 3 > 2
–
Z spans a larger range, and captures more information
X
Y
Z
Comparing Distributions
 How do we measure difference between two distributions ?
 Kullback-Leibler divergence:
–
dKL(p, q) = Ep[ h(q) – h(p) ] = Si pi log(pi/qi)
Inference
mechanism
Prior belief
Resulting belief
Comparing Distributions
 Kullback-Leibler divergence:
–
–
–
–
–
dKL(p, q) = Ep[ h(q) – h(p) ] = Si pi log(pi/qi)
dKL(p, q) >= 0
Captures extra information needed to capture p given q
Is asymmetric ! dKL(p, q) != dKL(q, p)
Is not a metric (does not satisfy triangle inequality)
 There are other measures:
–
2-distance, variational distance, f-divergences, …
Conditional Probability
 Given a joint distribution on random variables X, Y, how much
information about X can we glean from Y ?
 Conditional probability: p(X|Y)
p(X = x1 | Y = y1) = p(X = x1, Y = y1)/p(Y = y1)
–
X
Y
p(X,Y)
p(X|Y)
p(Y|X)
X
p(X)
x1
y1
0.25
1.0
0.5
x1
0.5
x1
y2
0.25
1.0
0.5
x2
0.25
x2
y3
0.25
0.5
1.0
x3
0.125
x3
y3
0.125
0.25
1.0
x4
0.125
x4
y3
0.125
0.25
1.0
Y
p(Y)
y1
0.25
y2
0.25
y3
0.5
Conditional Entropy
 Let h(x|y) = log2 1/p(x|y)
 H(X|Y) = Ex,y[h(x|y)] = Sx Sy p(x,y) log2 1/p(x|y)
 H(X|Y) = H(X,Y) – H(Y)
X
Y
p(X,Y)
p(X|Y)
h(X|Y)
x1
y1
0.25
1.0
0.0
x1
y2
0.25
1.0
0.0
x2
y3
0.25
0.5
1.0
x3
y3
0.125
0.25
2.0
x4
y3
0.125
0.25
2.0
 H(X|Y) = H(X,Y) – H(Y) = 2.25 – 1.5 = 0.75
 If X, Y are independent, H(X|Y) = H(X)
Mutual Information
 Mutual information captures the difference between the joint
distribution on X and Y, and the marginal distributions on X
and Y.
 Let i(x;y) = log p(x,y)/p(x)p(y)
 I(X;Y) = Ex,y[I(X;Y)] = Sx Sy p(x,y) log p(x,y)/p(x)p(y)
X
Y
p(X,Y)
h(X,Y)
i(X;Y)
X
p(X)
h(X)
x1
y1
0.25
2.0
1.0
Y
p(Y)
h(Y)
x1
0.5
1.0
x1
y2
0.25
2.0
1.0
y1
0.25
2.0
x2
0.25
2.0
x2
y3
0.25
2.0
1.0
y2
0.25
2.0
x3
0.125
3.0
x3
y3
0.125
3.0
1.0
y3
0.5
1.0
x4
0.125
3.0
x4
y3
0.125
3.0
1.0
Mutual Information: Strength of linkage
 I(X;Y) = H(X) + H(Y) – H(X,Y) = H(X) – H(X|Y) = H(Y) – H(Y|X)
 If X, Y are independent, then I(X;Y) = 0:
–
H(X,Y) = H(X) + H(Y), so I(X;Y) = H(X) + H(Y) – H(X,Y) = 0
 I(X;Y) <= max (H(X), H(Y))
Suppose Y = f(X) (deterministically)
– Then H(Y|X) = 0, and so I(X;Y) = H(Y) – H(Y|X) = H(Y)
–
 Mutual information captures higher-order interactions:
Covariance captures “linear” interactions only
– Two variables can be uncorrelated (covariance = 0) and have
nonzero mutual information:
– X R [-1,1], Y = X2. Cov(X,Y) = 0, I(X;Y) = H(X) > 0
–
Information-Theoretic Clustering
 Clustering takes a collection of objects and groups them.
Given a distance function between objects
– Choice of measure of complexity of clustering
– Choice of measure of cost for a cluster
–
 Usually,
Distance function is Euclidean distance
– Number of clusters is measure of complexity
– Cost measure for cluster is sum-of-squared-distance to center
–
 Goal: minimize complexity and cost
–
Inherent tradeoff between two
Feature Representation
Let V = {v1, v2, v3, v4}
X
X is “explained” by distribution over V.
v1
“Feature vector” of X is [0.5, 0.25, 0.125, 0.125]
v3
v2
v4
v1
v1
v2
aggregate
counts
v1
Column of data
X
f(X)
X
p(X)
v1
4
v1
0.5
v2
2
v2
0.25
v3
1
v3
0.125
v4
1
v4
0.125
Histogram
normalize
Probability
distribution
Feature Representation
V
v1
v2
v3
v4
X1
0.5
0.25
0.125
0.125
X2
0.5
0.2
0.15
0.15
X
p(v2|X2) = 0.2
Feature vector
Information-Theoretic Clustering
 Clustering takes a collection of objects and groups them.
Given a distance function between objects
– Choice of measure of complexity of clustering
– Choice of measure of cost for a cluster
–
 In information-theoretic setting
What is the distance function ?
– How do we measure complexity ?
– What is a notion of cost/quality ?
–
 Goal: minimize complexity and maximize quality
–
Inherent tradeoff between two
Measuring complexity of clustering
 Take 1: complexity of a clustering = #clusters
–
standard model of complexity.
 Doesn’t capture the fact that clusters have different sizes.

Measuring complexity of clustering
 Take 2: Complexity of clustering = number of bits needed to
describe it.
 Writing down “k” needs log k bits.
 In general, let cluster t  T have |t| elements.
set p(t) = |t|/n
– #bits to write down cluster sizes = H(T) = S pt log 1/pt
–
H(
) < H(
)
Information-theoretic Clustering (take I)
 Given data X = x1, ..., xn explained by variable V, partition X
into clusters (represented by T) such that
H(T) is minimized and quality is maximized
Soft clusterings
 In a “hard” clustering, each point is assigned to exactly one
cluster.
 Characteristic function
–
p(t|x) = 1 if x  t, 0 if not.
 Suppose we allow points to partially belong to clusters:
p(T|x) is a distribution.
– p(t|x) is the “probability” of assigning x to t
–
How do we describe the complexity of a clustering ?
Measuring complexity of clustering
 Take 1:
p(t) = Sx p(x) p(t|x)
– Compute H(T) as before.
–
 Problem:
T1
t1
t2
T2
t1
t2
x1
0.5
0.5
x1
0.99
0.01
x2
0.5
0.5
x2
0.01
0.99
h(T)
0.5
0.5
h(T)
0.5
0.5
H(T1) = H(T2) !!
Measuring complexity of clustering
 By averaging the memberships, we’ve lost useful information.
 Take II: Compute I(T;X) !
X
T1
p(X,T)
i(X;T)
X
T2
p(X,T)
i(X;T)
x1
t1
0.25
0
x1
t1
0.495
0.99
x1
t2
0.25
0
x1
t2
0.005
-5.64
x2
t1
0.25
0
x2
t1
0.25
0
x2
t2
0.25
0
x2
t2
0.25
0
I(T1;X) = 0
I(T2;X) = 0.46
 Even better: If T is a hard clustering of X, then I(T;X) = H(T)
Information-theoretic Clustering (take II)
 Given data X = x1, ..., xn explained by variable V, partition X
into clusters (represented by T) such that
I(T,X) is minimized and quality is maximized
Measuring cost of a cluster
Given objects Xt = {X1, X2, …, Xm} in cluster t,
Cost(t) = (1/m)Si d(Xi, C) = Si p(Xi) dKL(p(V|Xi), C)
where C = (1/m) Si p(V|Xi) = Si p(Xi) p(V|Xi) = p(V)
Mutual Information = Cost of Cluster
Cost(t) = (1/m)Si d(Xi, C) = Si p(Xi) dKL(p(V|Xi), p(V))
Si p(Xi) KL( p(V|Xi), p(V)) = Si p(Xi) Sj p(vj|Xi) log p(vj|Xi)/p(vj)
= Si,j p(Xi, vj) log p(vj, Xi)/p(vj)p(Xi)
= I(Xt, V) !!
Cost of a cluster = I(Xt,V)
Cost of a clustering
 If we partition X into k clusters X1, ..., Xk
Cost(clustering) = Si pi I(Xi, V)
(pi = |Xi|/|X|)
Cost of a clustering
 Each cluster center t can be “explained” in terms of V:
–
p(V|t) = Si p(Xi) p(V|Xi)
 Suppose we treat each cluster center itself as a point:
Cost of a clustering
 We can write down the “cost” of this “cluster”
–
Cost(T) = I(T;V)
 Key result [BMDG05] :
Cost(clustering) = I(X, V) – (T, V)
Minimizing cost(clustering) => maximizing I(T, V)
Information-theoretic Clustering (take III)
 Given data X = x1, ..., xn explained by variable V, partition X
into clusters (represented by T) such that
I(T;X) - bI(T;V) is maximized
 This is the Information Bottleneck Method [TPB98]
 Agglomerative techniques exist for the case of ‘hard’
clusterings
 b is the tradeoff parameter between complexity and cost
 I(T;X) and I(T;V) are in the same units.
Information Theory: Summary
 We can represent data as discrete distributions (normalized
histograms)
 Entropy captures uncertainty or information content in a
distribution
 The Kullback-Leibler distance captures the difference
between distributions
 Mutual information and conditional entropy capture linkage
between variables in a joint distribution
 We can formulate information-theoretic clustering problems
Outline
Part 1
 Introduction to Information Theory
 Application: Data Anonymization
 Application: Data Integration
Part 2




38
Review of Information Theory Basics
Application: Database Design
Computing Information Theoretic Primitives
Open Problems
Data Anonymization Using Randomization
 Goal: publish anonymized microdata to enable accurate ad hoc
analyses, but ensure privacy of individuals’ sensitive attributes
 Key ideas:
Randomize numerical data: add noise from known distribution
– Reconstruct original data distribution using published noisy data
–
 Issues:
How can the original data distribution be reconstructed?
– What kinds of randomization preserve privacy of individuals?
–
39
Information Theory for Data Management - Divesh & Suresh
Data Anonymization Using Randomization
 Many randomization strategies proposed [AS00, AA01, EGS03]
 Example randomization strategies: X in [0, 10]
R = X + μ (mod 11), μ is uniform in {-1, 0, 1}
– R = X + μ (mod 11), μ is in {-1 (p = 0.25), 0 (p = 0.5), 1 (p = 0.25)}
– R = X (p = 0.6), R = μ, μ is uniform in [0, 10] (p = 0.4)
–
 Question:
Which randomization strategy has higher privacy preservation?
– Quantify loss of privacy due to publication of randomized data
–
40
Information Theory for Data Management - Divesh & Suresh
Data Anonymization Using Randomization
 X in [0, 10], R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
41
Id
X
s1
0
s2
3
s3
5
s4
0
s5
8
s6
0
s7
6
s8
0
Information Theory for Data Management - Divesh & Suresh
Data Anonymization Using Randomization
 X in [0, 10], R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
42
Id
X
μ
Id
R1
s1
0
-1
s1
10
s2
3
0
s2
3
s3
5
1
s3
6
s4
0
0
s4
0
s5
8
1
s5
9
s6
0
-1
s6
10
s7
6
1
s7
7
s8
0
0
s8
0
→
Information Theory for Data Management - Divesh & Suresh
Data Anonymization Using Randomization
 X in [0, 10], R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
43
Id
X
μ
Id
R1
s1
0
0
s1
0
s2
3
-1
s2
2
s3
5
0
s3
5
s4
0
1
s4
1
s5
8
1
s5
9
s6
0
-1
s6
10
s7
6
-1
s7
5
s8
0
1
s8
1
→
Information Theory for Data Management - Divesh & Suresh
Reconstruction of Original Data Distribution
 X in [0, 10], R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
Reconstruct distribution of X using knowledge of R1 and μ
– EM algorithm converges to MLE of original distribution [AA01]
–
44
Id
X
μ
Id
R1
Id
X | R1
s1
0
0
s1
0
s1
{10, 0, 1}
s2
3
-1
s2
2
s2
{1, 2, 3}
s3
5
0
s3
5
s3
{4, 5, 6}
s4
0
1
s4
1
s4
{0, 1, 2}
s5
8
1
s5
9
s5
{8, 9, 10}
s6
0
-1
s6
10
s6
{9, 10, 0}
s7
6
-1
s7
5
s7
{4, 5, 6}
s8
0
1
s8
1
s8
{0, 1, 2}
→
→
Information Theory for Data Management - Divesh & Suresh
Analysis of Privacy [AS00]
 X in [0, 10], R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
–
45
If X is uniform in [0, 10], privacy determined by range of μ
Id
X
μ
Id
R1
Id
X | R1
s1
0
0
s1
0
s1
{10, 0, 1}
s2
3
-1
s2
2
s2
{1, 2, 3}
s3
5
0
s3
5
s3
{4, 5, 6}
s4
0
1
s4
1
s4
{0, 1, 2}
s5
8
1
s5
9
s5
{8, 9, 10}
s6
0
-1
s6
10
s6
{9, 10, 0}
s7
6
-1
s7
5
s7
{4, 5, 6}
s8
0
1
s8
1
s8
{0, 1, 2}
→
→
Information Theory for Data Management - Divesh & Suresh
Analysis of Privacy [AA01]
 X in [0, 10], R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
–
46
If X is uniform in [0, 1]  [5, 6], privacy smaller than range of μ
Id
X
μ
Id
R1
Id
X | R1
s1
0
0
s1
0
s1
{10, 0, 1}
s2
1
-1
s2
0
s2
{10, 0, 1}
s3
5
0
s3
5
s3
{4, 5, 6}
s4
6
1
s4
7
s4
{6, 7, 8}
s5
0
1
s5
1
s5
{0, 1, 2}
s6
1
-1
s6
0
s6
{10, 0, 1}
s7
5
-1
s7
4
s7
{3, 4, 5}
s8
6
1
s8
7
s8
{6, 7, 8}
→
→
Information Theory for Data Management - Divesh & Suresh
Analysis of Privacy [AA01]
 X in [0, 10], R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
If X is uniform in [0, 1]  [5, 6], privacy smaller than range of μ
– In some cases, sensitive value revealed
–
47
Id
X
μ
Id
R1
Id
X | R1
s1
0
0
s1
0
s1
{0, 1}
s2
1
-1
s2
0
s2
{0, 1}
s3
5
0
s3
5
s3
{5, 6}
s4
6
1
s4
7
s4
{6}
s5
0
1
s5
1
s5
{0, 1}
s6
1
-1
s6
0
s6
{0, 1}
s7
5
-1
s7
4
s7
{5}
s8
6
1
s8
7
s8
{6}
→
→
Information Theory for Data Management - Divesh & Suresh
Quantify Loss of Privacy [AA01]
 Goal: quantify loss of privacy based on mutual information I(X;R)
Smaller H(X|R)  more loss of privacy in X by knowledge of R
– Larger I(X;R)  more loss of privacy in X by knowledge of R
– I(X;R) = H(X) – H(X|R)
–
 I(X;R) used to capture correlation between X and R
p(X) is the prior knowledge of sensitive attribute X
– p(X, R) is the joint distribution of X and R
–
48
Information Theory for Data Management - Divesh & Suresh
Quantify Loss of Privacy [AA01]
 Goal: quantify loss of privacy based on mutual information I(X;R)
–
X is uniform in [5, 6], R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
X
R1 p(X,R1) h(X,R1) i(X;R1)
X
5
4
5
5
5
6
5
6
6
5
R1
6
6
4
6
7
5
p(X)
h(X)
p(R1)
h(R1)
6
7
49
Information Theory for Data Management - Divesh & Suresh
Quantify Loss of Privacy [AA01]
 Goal: quantify loss of privacy based on mutual information I(X;R)
–
50
X is uniform in [5, 6], R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
X
R1 p(X,R1) h(X,R1) i(X;R1)
X
p(X)
5
4
0.17
5
0.5
5
5
0.17
6
0.5
5
6
0.17
6
5
0.17
R1
p(R1)
6
6
0.17
4
0.17
6
7
0.17
5
0.34
6
0.34
7
0.17
h(X)
h(R1)
Information Theory for Data Management - Divesh & Suresh
Quantify Loss of Privacy [AA01]
 Goal: quantify loss of privacy based on mutual information I(X;R)
–
51
X is uniform in [5, 6], R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
X
R1 p(X,R1) h(X,R1) i(X;R1)
X
p(X)
h(X)
5
4
0.17
2.58
5
0.5
1.0
5
5
0.17
2.58
6
0.5
1.0
5
6
0.17
2.58
6
5
0.17
2.58
R1
p(R1)
h(R1)
6
6
0.17
2.58
4
0.17
2.58
6
7
0.17
2.58
5
0.34
1.58
6
0.34
1.58
7
0.17
2.58
Information Theory for Data Management - Divesh & Suresh
Quantify Loss of Privacy [AA01]
 Goal: quantify loss of privacy based on mutual information I(X;R)
X is uniform in [5, 6], R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
– I(X;R) = 0.33
–
52
X
R1 p(X,R1) h(X,R1) i(X;R1)
X
p(X)
h(X)
5
4
0.17
2.58
1.0
5
0.5
1.0
5
5
0.17
2.58
0.0
6
0.5
1.0
5
6
0.17
2.58
0.0
6
5
0.17
2.58
0.0
R1
p(R1)
h(R1)
6
6
0.17
2.58
0.0
4
0.17
2.58
6
7
0.17
2.58
1.0
5
0.34
1.58
6
0.34
1.58
7
0.17
2.58
Information Theory for Data Management - Divesh & Suresh
Quantify Loss of Privacy [AA01]
 Goal: quantify loss of privacy based on mutual information I(X;R)
X is uniform in [5, 6], R2 = X + μ (mod 11), μ is uniform in {0, 1}
– I(X;R1) = 0.33, I(X;R2) = 0.5  R2 is a bigger privacy risk than R1
–
53
X
R2 p(X,R2) h(X,R2) i(X;R2)
X
p(X)
h(X)
5
5
0.25
2.0
1.0
5
0.5
1.0
5
6
0.25
2.0
0.0
6
0.5
1.0
6
6
0.25
2.0
0.0
6
7
0.25
2.0
1.0
R2
p(R2)
h(R2)
5
0.25
2.0
6
0.5
1.0
7
0.25
2.0
Information Theory for Data Management - Divesh & Suresh
Quantify Loss of Privacy [AA01]
 Equivalent goal: quantify loss of privacy based on H(X|R)
X is uniform in [5, 6], R2 = X + μ (mod 11), μ is uniform in {0, 1}
– Intuition: we know more about X given R2, than about X given R1
– H(X|R1) = 0.67, H(X|R2) = 0.5  R2 is a bigger privacy risk than R1
–
54
X
R1 p(X,R1) p(X|R1) h(X|R1)
X
R2 p(X,R2) p(X|R2) h(X|R2)
5
4
0.17
1.0
0.0
5
5
0.25
1.0
0.0
5
5
0.17
0.5
1.0
5
6
0.25
0.5
1.0
5
6
0.17
0.5
1.0
6
6
0.25
0.5
1.0
6
5
0.17
0.5
1.0
6
7
0.25
1.0
0.0
6
6
0.17
0.5
1.0
6
7
0.17
1.0
0.0
Information Theory for Data Management - Divesh & Suresh
Quantify Loss of Privacy
 Example: X is uniform in [0, 1]
R3 = e (p = 0.9999), R3 = X (p = 0.0001)
– R4 = X (p = 0.6), R4 = 1 – X (p = 0.4)
–
 Is R3 or R4 a bigger privacy risk?
55
Information Theory for Data Management - Divesh & Suresh
Worst Case Loss of Privacy [EGS03]
 Example: X is uniform in [0, 1]
R3 = e (p = 0.9999), R3 = X (p = 0.0001)
– R4 = X (p = 0.6), R4 = 1 – X (p = 0.4)
–
X
R3
p(X,R3)
h(X,R3)
i(X;R3)
X
R4 p(X,R4) h(X,R4) i(X;R4)
0
e
0.49995
1.0
0.0
0
0
0.3
1.74
0.26
0
0
0.00005
14.29
1.0
0
1
0.2
2.32
-0.32
1
e
0.49995
1.0
0.0
1
0
0.2
2.32
-0.32
1
1
0.00005
14.29
1.0
1
1
0.3
1.74
0.26
 I(X;R3) = 0.0001 << I(X;R4) = 0.028
56
Information Theory for Data Management - Divesh & Suresh
Worst Case Loss of Privacy [EGS03]
 Example: X is uniform in [0, 1]
R3 = e (p = 0.9999), R3 = X (p = 0.0001)
– R4 = X (p = 0.6), R4 = 1 – X (p = 0.4)
–
X
R3
p(X,R3)
h(X,R3)
i(X;R3)
X
R4 p(X,R4) h(X,R4) i(X;R4)
0
e
0.49995
1.0
0.0
0
0
0.3
1.74
0.26
0
0
0.00005
14.29
1.0
0
1
0.2
2.32
-0.32
1
e
0.49995
1.0
0.0
1
0
0.2
2.32
-0.32
1
1
0.00005
14.29
1.0
1
1
0.3
1.74
0.26
 I(X;R3) = 0.0001 << I(X;R4) = 0.028
–
57
But R3 has a larger worst case risk
Information Theory for Data Management - Divesh & Suresh
Worst Case Loss of Privacy [EGS03]
 Goal: quantify worst case loss of privacy in X by knowledge of R
–
Use max KL divergence, instead of mutual information
 Mutual information can be formulated as expected KL divergence
I(X;R) = ∑x ∑r p(x,r)*log2(p(x,r)/p(x)*p(r)) = KL(p(X,R) ||p(X)*p(R))
– I(X;R) = ∑r p(r) ∑x p(x|r)*log2(p(x|r)/p(x)) = ER [KL(p(X|r) ||p(X))]
– [AA01] measure quantifies expected loss of privacy over R
–
 [EGS03] propose a measure based on worst case loss of privacy
–
58
IW(X;R) = MAXR [KL(p(X|r) ||p(X))]
Information Theory for Data Management - Divesh & Suresh
Worst Case Loss of Privacy [EGS03]
 Example: X is uniform in [0, 1]
R3 = e (p = 0.9999), R3 = X (p = 0.0001)
– R4 = X (p = 0.6), R4 = 1 – X (p = 0.4)
–
X
R3
p(X,R3)
p(X|R3)
i(X;R3)
X
R4 p(X,R4) p(X|R4) i(X;R4)
0
e
0.49995
0.5
0.0
0
0
0.3
0.6
0.26
0
0
0.00005
1.0
1.0
0
1
0.2
0.4
-0.32
1
e
0.49995
0.5
0.0
1
0
0.2
0.4
-0.32
1
1
0.00005
1.0
1.0
1
1
0.3
0.6
0.26
 IW(X;R3) = max{0.0, 1.0, 1.0} > IW(X;R4) = max{0.028, 0.028}
59
Information Theory for Data Management - Divesh & Suresh
Worst Case Loss of Privacy [EGS03]
 Example: X is uniform in [5, 6]
R1 = X + μ (mod 11), μ is uniform in {-1, 0, 1}
– R2 = X + μ (mod 11), μ is uniform in {0, 1}
–
X
R1 p(X,R1) p(X|R1)
i(X;R1)
X
R2 p(X,R2) p(X|R2)
5
4
0.17
5
5
5
i(X;R2)
1.0
1.0
5
5
0.25
1.0
1.0
0.17
0.5
0.0
5
6
0.25
0.5
0.0
6
0.17
0.5
0.0
6
6
0.25
0.5
0.0
6
5
0.17
0.5
0.0
6
7
0.25
1.0
1.0
6
6
0.17
0.5
0.0
6
7
0.17
1.0
1.0
 IW(X;R1) = max{1.0, 0.0, 0.0, 1.0} = IW(X;R2) = {1.0, 0.0, 1.0}
–
60
Unable to capture that R2 is a bigger privacy risk than R1
Information Theory for Data Management - Divesh & Suresh
Data Anonymization: Summary
 Randomization techniques useful for microdata anonymization
–
Randomization techniques differ in their loss of privacy
 Information theoretic measures useful to capture loss of privacy
Expected KL divergence captures expected loss of privacy [AA01]
– Maximum KL divergence captures worst case loss of privacy [EGS03]
– Both are useful in practice
–
61
Information Theory for Data Management - Divesh & Suresh
Outline
Part 1
 Introduction to Information Theory
 Application: Data Anonymization
 Application: Data Integration
Part 2




62
Review of Information Theory Basics
Application: Database Design
Computing Information Theoretic Primitives
Open Problems
Information Theory for Data Management - Divesh & Suresh
Schema Matching
 Goal: align columns across database tables to be integrated
–
Fundamental problem in database integration
 Early useful approach: textual similarity of column names
False positives: Address ≠ IP_Address
– False negatives: Customer_Id = Client_Number
–
 Early useful approach: overlap of values in columns, e.g., Jaccard
False positives: Emp_Id ≠ Project_Id
– False negatives: Emp_Id = Personnel_Number
–
63
Information Theory for Data Management - Divesh & Suresh
Opaque Schema Matching [KN03]
 Goal: align columns when column names, data values are opaque
Databases belong to different government bureaucracies 
– Treat column names and data values as uninterpreted (generic)
–
A
B
C
D
W
X
Y
Z
a1
b2
c1
d1
w2
x1
y1
z2
a3
b4
c2
d2
w4
x2
y3
z3
a1
b1
c1
d2
w3
x3
y3
z1
a4
b3
c2
d3
w1
x2
y1
z2
 Example: EMP_PROJ(Emp_Id, Proj_Id, Task_Id, Status_Id)
Likely that all Id fields are from the same domain
– Different databases may have different column names
–
64
Information Theory for Data Management - Divesh & Suresh
Opaque Schema Matching [KN03]
 Approach: build complete, labeled graph GD for each database D
Nodes are columns, label(node(X)) = H(X), label(edge(X, Y)) = I(X;Y)
– Perform graph matching between GD1 and GD2, minimizing distance
–
 Intuition:
Entropy H(X) captures distribution of values in database column X
– Mutual information I(X;Y) captures correlations between X, Y
– Efficiency: graph matching between schema-sized graphs
–
65
Information Theory for Data Management - Divesh & Suresh
Opaque Schema Matching [KN03]
 Approach: build complete, labeled graph GD for each database D
–
66
Nodes are columns, label(node(X)) = H(X), label(edge(X, Y)) = I(X;Y)
A
B
C
D
A
p(A)
B
p(B)
C
p(C)
D
p(D)
a1
b2
c1
d1
a1
0.5
b1
0.25
c1
0.5
d1
0.25
a3
b4
c2
d2
a3
0.25
b2
0.25
c2
0.5
d2
0.5
a1
b1
c1
d2
a4
0.25
b3
0.25
d3
0.25
a4
b3
c2
d3
b4
0.25
Information Theory for Data Management - Divesh & Suresh
Opaque Schema Matching [KN03]
 Approach: build complete, labeled graph GD for each database D
–
Nodes are columns, label(node(X)) = H(X), label(edge(X, Y)) = I(X;Y)
A
B
C
D
A
h(A)
B
h(B)
C
h(C)
D
h(D)
a1
b2
c1
d1
a1
1.0
b1
2.0
c1
1.0
d1
2.0
a3
b4
c2
d2
a3
2.0
b2
2.0
c2
1.0
d2
1.0
a1
b1
c1
d2
a4
2.0
b3
2.0
d3
2.0
a4
b3
c2
d3
b4
2.0
 H(A) = 1.5, H(B) = 2.0, H(C) = 1.0, H(D) = 1.5
67
Information Theory for Data Management - Divesh & Suresh
Opaque Schema Matching [KN03]
 Approach: build complete, labeled graph GD for each database D
–
Nodes are columns, label(node(X)) = H(X), label(edge(X, Y)) = I(X;Y)
A
B
C
D
A
h(A)
B
h(B)
A
B
h(A,B)
i(A;B)
a1
b2
c1
d1
a1
1.0
b1
2.0
a1
b2
2.0
1.0
a3
b4
c2
d2
a3
2.0
b2
2.0
a3
b4
2.0
2.0
a1
b1
c1
d2
a4
2.0
b3
2.0
a1
b1
2.0
1.0
a4
b3
c2
d3
b4
2.0
a4
b3
2.0
2.0
 H(A) = 1.5, H(B) = 2.0, H(C) = 1.0, H(D) = 1.5, I(A;B) = 1.5
68
Information Theory for Data Management - Divesh & Suresh
Opaque Schema Matching [KN03]
 Approach: build complete, labeled graph GD for each database D
–
Nodes are columns, label(node(X)) = H(X), label(edge(X, Y)) = I(X;Y)
A
B
C
D
a1
b2
c1
d1
a3
b4
c2
d2
a1
b1
c1
d2
a4
b3
c2
d3
1.5
1.5
A
B
1.0
1.0
1.5
1.0
1.0
C
D
0.5
69
2.0
Information Theory for Data Management - Divesh & Suresh
1.5
Opaque Schema Matching [KN03]
 Approach: build complete, labeled graph GD for each database D
Nodes are columns, label(node(X)) = H(X), label(edge(X, Y)) = I(X;Y)
– Perform graph matching between GD1 and GD2, minimizing distance
–
1.5
1.5
A
B
2.0
2.0
1.0
1.5
1.0
1.5
1.0
1.0
0.5
C
D
0.5
1.5
1.0
Y
Z
1.0
 [KN03] uses euclidean and normal distance metrics
70
X
1.5
1.0
1.0
1.5
W
Information Theory for Data Management - Divesh & Suresh
1.5
Opaque Schema Matching [KN03]
 Approach: build complete, labeled graph GD for each database D
Nodes are columns, label(node(X)) = H(X), label(edge(X, Y)) = I(X;Y)
– Perform graph matching between GD1 and GD2, minimizing distance
–
1.5
1.5
A
B
2.0
2.0
1.0
1.5
1.0
1.5
1.0
1.0
0.5
C
D
0.5
71
X
1.5
1.0
1.0
1.5
W
1.5
1.0
Y
Z
1.0
Information Theory for Data Management - Divesh & Suresh
1.5
Opaque Schema Matching [KN03]
 Approach: build complete, labeled graph GD for each database D
Nodes are columns, label(node(X)) = H(X), label(edge(X, Y)) = I(X;Y)
– Perform graph matching between GD1 and GD2, minimizing distance
–
1.5
1.5
A
B
2.0
2.0
1.0
1.5
1.0
1.5
1.0
1.0
0.5
C
D
0.5
72
X
1.5
1.0
1.0
1.5
W
1.5
1.0
Y
Z
1.0
Information Theory for Data Management - Divesh & Suresh
1.5
Heterogeneity Identification [DKOSV06]
 Goal: identify columns with semantically heterogeneous values
–
Can arise due to opaque schema matching [KN03]
 Key ideas:
Heterogeneity based on distribution, distinguishability of values
– Use Information Bottleneck to compute soft clustering of values
–
 Issues:
Which information theoretic measure characterizes heterogeneity?
– How to set parameters in the Information Bottleneck method?
–
73
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Example: semantically homogeneous, heterogeneous columns
74
Customer_Id
Customer_Id
h8742@yyy.com
h8742@yyy.com
kkjj+@haha.org
kkjj+@haha.org
qwerty@keyboard.us
qwerty@keyboard.us
555-1212@fax.in
555-1212@fax.in
alpha@beta.ga
(908)-555-1234
john.smith@noname.org
973-360-0000
jane.doe@1973law.us
360-0007
jamesbond.007@action.com
(877)-807-4596
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Example: semantically homogeneous, heterogeneous columns
75
Customer_Id
Customer_Id
h8742@yyy.com
h8742@yyy.com
kkjj+@haha.org
kkjj+@haha.org
qwerty@keyboard.us
qwerty@keyboard.us
555-1212@fax.in
555-1212@fax.in
alpha@beta.ga
(908)-555-1234
john.smith@noname.org
973-360-0000
jane.doe@1973law.us
360-0007
jamesbond.007@action.com
(877)-807-4596
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Example: semantically homogeneous, heterogeneous columns
Customer_Id
Customer_Id
h8742@yyy.com
h8742@yyy.com
kkjj+@haha.org
kkjj+@haha.org
qwerty@keyboard.us
qwerty@keyboard.us
555-1212@fax.in
555-1212@fax.in
alpha@beta.ga
(908)-555-1234
john.smith@noname.org
973-360-0000
jane.doe@1973law.us
360-0007
jamesbond.007@action.com
(877)-807-4596
 More semantic types in column  greater heterogeneity
–
76
Only email versus email + phone
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Example: semantically homogeneous, heterogeneous columns
77
Customer_Id
Customer_Id
h8742@yyy.com
h8742@yyy.com
kkjj+@haha.org
kkjj+@haha.org
qwerty@keyboard.us
qwerty@keyboard.us
555-1212@fax.in
555-1212@fax.in
alpha@beta.ga
(908)-555-1234
john.smith@noname.org
973-360-0000
jane.doe@1973law.us
360-0007
(877)-807-4596
(877)-807-4596
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Example: semantically homogeneous, heterogeneous columns
Customer_Id
Customer_Id
h8742@yyy.com
h8742@yyy.com
kkjj+@haha.org
kkjj+@haha.org
qwerty@keyboard.us
qwerty@keyboard.us
555-1212@fax.in
555-1212@fax.in
alpha@beta.ga
(908)-555-1234
john.smith@noname.org
973-360-0000
jane.doe@1973law.us
360-0007
(877)-807-4596
(877)-807-4596
 Relative distribution of semantic types impacts heterogeneity
–
78
Mainly email + few phone versus balanced email + phone
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Example: semantically homogeneous, heterogeneous columns
79
Customer_Id
Customer_Id
187-65-2468
h8742@yyy.com
987-64-6837
kkjj+@haha.org
789-15-4321
qwerty@keyboard.us
987-65-4321
555-1212@fax.in
(908)-555-1234
(908)-555-1234
973-360-0000
973-360-0000
360-0007
360-0007
(877)-807-4596
(877)-807-4596
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Example: semantically homogeneous, heterogeneous columns
80
Customer_Id
Customer_Id
187-65-2468
h8742@yyy.com
987-64-6837
kkjj+@haha.org
789-15-4321
qwerty@keyboard.us
987-65-4321
555-1212@fax.in
(908)-555-1234
(908)-555-1234
973-360-0000
973-360-0000
360-0007
360-0007
(877)-807-4596
(877)-807-4596
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Example: semantically homogeneous, heterogeneous columns
Customer_Id
Customer_Id
187-65-2468
h8742@yyy.com
987-64-6837
kkjj+@haha.org
789-15-4321
qwerty@keyboard.us
987-65-4321
555-1212@fax.in
(908)-555-1234
(908)-555-1234
973-360-0000
973-360-0000
360-0007
360-0007
(877)-807-4596
(877)-807-4596
 More easily distinguished types  greater heterogeneity
–
81
Phone + (possibly) SSN versus balanced email + phone
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Heterogeneity = space complexity of soft clustering of the data
More, balanced clusters  greater heterogeneity
– More distinguishable clusters  greater heterogeneity
–
 Soft clustering
Soft  assign probabilities to membership of values in clusters
– How many clusters: tradeoff between space versus quality
– Use Information Bottleneck to compute soft clustering of values
–
82
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Hard clustering
X = Customer_Id T = Cluster_Id
83
187-65-2468
t1
987-64-6837
t1
789-15-4321
t1
987-65-4321
t1
(908)-555-1234
t2
973-360-0000
t1
360-0007
t3
(877)-807-4596
t2
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Soft clustering: cluster membership probabilities
X = Customer_Id T = Cluster_Id p(T|X)
789-15-4321
t1
0.75
987-65-4321
t1
0.75
789-15-4321
t2
0.25
987-65-4321
t2
0.25
(908)-555-1234
t1
0.25
973-360-0000
t1
0.5
(908)-555-1234
t2
0.75
973-360-0000
t2
0.5
 How to compute a good soft clustering?
84
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Represent strings as q-gram distributions
85
Customer_Id
X = Customer_Id
V = 4-grams
p(X,V)
187-65-2468
987-65-4321
987-
0.10
987-64-6837
987-65-4321
87-6
0.13
789-15-4321
987-65-4321
7-65
0.12
987-65-4321
987-65-4321
-65-
0.15
(908)-555-1234
987-65-4321
65-4
0.05
973-360-0000
987-65-4321
5-43
0.20
360-0007
987-65-4321
-432
0.15
(877)-807-4596
987-65-4321
4321
0.10
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 iIB: find soft clustering T of X that minimizes I(T;X) – β*I(T;V)
Customer_Id
X = Customer_Id
V = 4-grams
p(X,V)
187-65-2468
987-65-4321
987-
0.10
987-64-6837
987-65-4321
87-6
0.13
789-15-4321
987-65-4321
7-65
0.12
987-65-4321
987-65-4321
-65-
0.15
(908)-555-1234
987-65-4321
65-4
0.05
973-360-0000
987-65-4321
5-43
0.20
360-0007
987-65-4321
-432
0.15
(877)-807-4596
987-65-4321
4321
0.10
 Allow iIB to use arbitrarily many clusters, use β* = H(X)/I(X;V)
–
86
Closest to point with minimum space and maximum quality
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Rate distortion curve: I(T;V)/I(X;V) vs I(T;X)/H(X)
β*
87
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Heterogeneity = mutual information I(T;X) of iIB clustering T at β*
X = Customer_Id T = Cluster_Id p(T|X) i(T;X)
789-15-4321
t1
0.75
0.41
987-65-4321
t1
0.75
0.41
789-15-4321
t2
0.25
-0.81
987-65-4321
t2
0.25
-0.81
(908)-555-1234
t1
0.25
-1.17
973-360-0000
t1
0.5
-0.17
(908)-555-1234
t2
0.75
0.77
973-360-0000
t2
0.5
0.19
 0 ≤I(T;X) (= 0.126) ≤ H(X) (= 2.0), H(T) (= 1.0)
–
88
Ideally use iIB with an arbitrarily large number of clusters in T
Information Theory for Data Management - Divesh & Suresh
Heterogeneity Identification [DKOSV06]
 Heterogeneity = mutual information I(T;X) of iIB clustering T at β*
89
Information Theory for Data Management - Divesh & Suresh
Data Integration: Summary
 Analyzing database instance critical for effective data integration
–
Matching and quality assessments are key components
 Information theoretic measures useful for schema matching
Align columns when column names, data values are opaque
– Mutual information I(X;V) captures correlations between X, V
–
 Information theoretic measures useful for heterogeneity testing
Identify columns with semantically heterogeneous values
– I(T;X) of iIB clustering T at β* captures column heterogeneity
–
90
Information Theory for Data Management - Divesh & Suresh
Outline
Part 1
 Introduction to Information Theory
 Application: Data Anonymization
 Application: Data Integration
Part 2




91
Review of Information Theory Basics
Application: Database Design
Computing Information Theoretic Primitives
Open Problems
Information Theory for Data Management - Divesh & Suresh
Review of Information Theory Basics
 Discrete distribution: probability p(X)
X
Y
Z
p(X,Y,Z)
X
Y
p(X,Y)
X
p(X)
x1
y1
z1
0.125
x1
y1
0.25
x1
0.5
x1
y2
z2
0.125
x1
y2
0.25
x2
0.25
x1
y1
z2
0.125
x2
y3
0.25
x3
0.125
x1
y2
z1
0.125
x3
y3
0.125
x4
0.125
x2
y3
z3
0.125
x4
y3
0.125
x2
y3
z4
0.125
Y
p(Y)
x3
y3
z5
0.125
y1
0.25
x4
y3
z6
0.125
y2
0.25
y3
0.5
 p(X,Y) = ∑z p(X,Y,Z=z)
92
Information Theory for Data Management - Divesh & Suresh
Review of Information Theory Basics
 Discrete distribution: probability p(X)
X
Y
Z
p(X,Y,Z)
X
Y
p(X,Y)
X
p(X)
x1
y1
z1
0.125
x1
y1
0.25
x1
0.5
x1
y2
z2
0.125
x1
y2
0.25
x2
0.25
x1
y1
z2
0.125
x2
y3
0.25
x3
0.125
x1
y2
z1
0.125
x3
y3
0.125
x4
0.125
x2
y3
z3
0.125
x4
y3
0.125
x2
y3
z4
0.125
Y
p(Y)
x3
y3
z5
0.125
y1
0.25
x4
y3
z6
0.125
y2
0.25
y3
0.5
 p(Y) = ∑x p(X=x,Y) = ∑x ∑z p(X=x,Y,Z=z)
93
Information Theory for Data Management - Divesh & Suresh
Review of Information Theory Basics
 Discrete distribution: conditional probability p(X|Y)
X
Y
p(X,Y)
p(X|Y)
p(Y|X)
X
p(X)
x1
y1
0.25
1.0
0.5
x1
0.5
x1
y2
0.25
1.0
0.5
x2
0.25
x2
y3
0.25
0.5
1.0
x3
0.125
x3
y3
0.125
0.25
1.0
x4
0.125
x4
y3
0.125
0.25
1.0
Y
p(Y)
y1
0.25
y2
0.25
y3
0.5
 p(X,Y) = p(X|Y)*p(Y) = p(Y|X)*p(X)
94
Information Theory for Data Management - Divesh & Suresh
Review of Information Theory Basics
 Discrete distribution: entropy H(X)
X
Y
p(X,Y)
h(X,Y)
X
p(X)
h(X)
x1
y1
0.25
2.0
x1
0.5
1.0
x1
y2
0.25
2.0
x2
0.25
2.0
x2
y3
0.25
2.0
x3
0.125
3.0
x3
y3
0.125
3.0
x4
0.125
3.0
x4
y3
0.125
3.0
Y
p(Y)
h(Y)
y1
0.25
2.0
y2
0.25
2.0
y3
0.5
1.0
 h(x) = log2(1/p(x))
H(X) = ∑X=x p(x)*h(x) = 1.75
– H(Y) = ∑Y=y p(y)*h(y) = 1.5 (≤ log2(|Y|) = 1.58)
– H(X,Y) = ∑X=x ∑Y=y p(x,y)*h(x,y) = 2.25 (≤ log2(|X,Y|) = 2.32)
–
95
Information Theory for Data Management - Divesh & Suresh
Review of Information Theory Basics
 Discrete distribution: conditional entropy H(X|Y)
X
Y
p(X,Y)
p(X|Y)
h(X|Y)
X
p(X)
h(X)
x1
y1
0.25
1.0
0.0
x1
0.5
1.0
x1
y2
0.25
1.0
0.0
x2
0.25
2.0
x2
y3
0.25
0.5
1.0
x3
0.125
3.0
x3
y3
0.125
0.25
2.0
x4
0.125
3.0
x4
y3
0.125
0.25
2.0
Y
p(Y)
h(Y)
y1
0.25
2.0
y2
0.25
2.0
y3
0.5
1.0
 h(x|y) = log2(1/p(x|y))
H(X|Y) = ∑X=x ∑Y=y p(x,y)*h(x|y) = 0.75
– H(X|Y) = H(X,Y) – H(Y) = 2.25 – 1.5
–
96
Information Theory for Data Management - Divesh & Suresh
Review of Information Theory Basics
 Discrete distribution: mutual information I(X;Y)
X
Y
p(X,Y)
h(X,Y)
i(X;Y)
X
p(X)
h(X)
x1
y1
0.25
2.0
1.0
x1
0.5
1.0
x1
y2
0.25
2.0
1.0
x2
0.25
2.0
x2
y3
0.25
2.0
1.0
x3
0.125
3.0
x3
y3
0.125
3.0
1.0
x4
0.125
3.0
x4
y3
0.125
3.0
1.0
Y
p(Y)
h(Y)
y1
0.25
2.0
y2
0.25
2.0
y3
0.5
1.0
 i(x;y) = log2(p(x,y)/p(x)*p(y))
I(X;Y) = ∑X=x ∑Y=y p(x,y)*i(x;y) = 1.0
– I(X;Y) = H(X) + H(Y) – H(X,Y) = 1.75 + 1.5 – 2.25
–
97
Information Theory for Data Management - Divesh & Suresh
Outline
Part 1
 Introduction to Information Theory
 Application: Data Anonymization
 Application: Data Integration
Part 2




98
Review of Information Theory Basics
Application: Database Design
Computing Information Theoretic Primitives
Open Problems
Information Theory for Data Management - Divesh & Suresh
Information Dependencies [DR00]
 Goal: use information theory to examine and reason about
information content of the attributes in a relation instance
 Key ideas:
Novel InD measure between attribute sets X, Y based on H(Y|X)
– Identify numeric inequalities between InD measures
–
 Results:
InD measures are a broader class than FDs and MVDs
– Armstrong axioms for FDs derivable from InD inequalities
– MVD inference rules derivable from InD inequalities
–
99
Information Theory for Data Management - Divesh & Suresh
Information Dependencies [DR00]
 Functional dependency: X → Y
–
100
FD X → Y holds iff  t1, t2 ((t1[X] = t2[X])  (t1[Y] = t2[Y]))
X
Y
Z
x1
y1
z1
x1
y2
z2
x1
y1
z2
x1
y2
z1
x2
y3
z3
x2
y3
z4
x3
y3
z5
x4
y3
z6
Information Theory for Data Management - Divesh & Suresh
Information Dependencies [DR00]
 Functional dependency: X → Y
–
101
FD X → Y holds iff  t1, t2 ((t1[X] = t2[X])  (t1[Y] = t2[Y]))
X
Y
Z
x1
y1
z1
x1
y2
z2
x1
y1
z2
x1
y2
z1
x2
y3
z3
x2
y3
z4
x3
y3
z5
x4
y3
z6
Information Theory for Data Management - Divesh & Suresh
Information Dependencies [DR00]
 Result: FD X → Y holds iff H(Y|X) = 0
–
Intuition: once X is known, no remaining uncertainty in Y
X
Y
p(X,Y)
p(Y|X)
h(Y|X)
X
p(X)
x1
y1
0.25
0.5
1.0
x1
0.5
x1
y2
0.25
0.5
1.0
x2
0.25
x2
y3
0.25
1.0
0.0
x3
0.125
x3
y3
0.125
1.0
0.0
x4
0.125
x4
y3
0.125
1.0
0.0
Y
p(Y)
y1
0.25
y2
0.25
y3
0.5
 H(Y|X) = 0.5
102
Information Theory for Data Management - Divesh & Suresh
Information Dependencies [DR00]
 Multi-valued dependency: X →→ Y
–
103
MVD X →
→ Y holds iff R(X,Y,Z) = R(X,Y)
X
Y
Z
x1
y1
z1
x1
y2
z2
x1
y1
z2
x1
y2
z1
x2
y3
z3
x2
y3
z4
x3
y3
z5
x4
y3
z6
R(X,Z)
Information Theory for Data Management - Divesh & Suresh
Information Dependencies [DR00]
 Multi-valued dependency: X →→ Y
–
104
MVD X →
→ Y holds iff R(X,Y,Z) = R(X,Y)
R(X,Z)
X
Y
Z
X
Y
X
Z
x1
y1
z1
x1
y1
x1
z1
x1
y2
z2
x1
y2
x1
z2
x1
y1
z2
x2
y3
x2
z3
x1
y2
z1
x3
y3
x2
z4
x2
y3
z3
x4
y3
x3
z5
x2
y3
z4
x4
z6
x3
y3
z5
x4
y3
z6
=
Information Theory for Data Management - Divesh & Suresh
Information Dependencies [DR00]
 Multi-valued dependency: X →→ Y
–
105
MVD X →
→ Y holds iff R(X,Y,Z) = R(X,Y)
R(X,Z)
X
Y
Z
X
Y
X
Z
x1
y1
z1
x1
y1
x1
z1
x1
y2
z2
x1
y2
x1
z2
x1
y1
z2
x2
y3
x2
z3
x1
y2
z1
x3
y3
x2
z4
x2
y3
z3
x4
y3
x3
z5
x2
y3
z4
x4
z6
x3
y3
z5
x4
y3
z6
=
Information Theory for Data Management - Divesh & Suresh
Information Dependencies [DR00]
 Result: MVD X →→ Y holds iff H(Y,Z|X) = H(Y|X) + H(Z|X)
–
Intuition: once X known, uncertainties in Y and Z are independent
X
Y
Z
h(Y,Z|X)
X
Y
h(Y|X)
X
Z
h(Z|X)
x1
y1
z1
2.0
x1
y1
1.0
x1
z1
1.0
x1
y2
z2
2.0
x1
y2
1.0
x1
z2
1.0
x1
y1
z2
2.0
x2
y3
0.0
x2
z3
1.0
x1
y2
z1
2.0
x3
y3
0.0
x2
z4
1.0
x2
y3
z3
1.0
x4
y3
0.0
x3
z5
0.0
x2
y3
z4
1.0
x4
z6
0.0
x3
y3
z5
0.0
x4
y3
z6
0.0
=
 H(Y|X) = 0.5, H(Z|X) = 0.75, H(Y,Z|X) = 1.25
106
Information Theory for Data Management - Divesh & Suresh
Information Dependencies [DR00]
 Result: Armstrong axioms for FDs derivable from InD inequalities
 Reflexivity: If Y  X, then X → Y
–
H(Y|X) = 0 for Y  X
 Augmentation: X → Y  X,Z → Y,Z
–
0 ≤ H(Y,Z|X,Z) = H(Y|X,Z) ≤ H(Y|X) = 0
 Transitivity: X → Y & Y → Z  X → Z
–
107
0 ≥ H(Y|X) + H(Z|Y) ≥ H(Z|X) ≥ 0
Information Theory for Data Management - Divesh & Suresh
Database Normal Forms
 Goal: eliminate update anomalies by good database design
–
Need to know the integrity constraints on all database instances
 Boyce-Codd normal form:
Input: a set ∑ of functional dependencies
– For every (non-trivial) FD R.X → R.Y  ∑+, R.X is a key of R
–
 4NF:
Input: a set ∑ of functional and multi-valued dependencies
– For every (non-trivial) MVD R.X →
→ R.Y  ∑+, R.X is a key of R
–
108
Information Theory for Data Management - Divesh & Suresh
Database Normal Forms
 Functional dependency: X → Y
–
109
Which design is better?
X
Y
Z
X
Y
X
Z
x1
y1
z1
x1
y1
x1
z1
x1
y1
z2
x2
y2
x1
z2
x2
y2
z3
x3
y3
x2
z3
x2
y2
z4
x4
y4
x2
z4
x3
y3
z5
x3
z5
x4
y4
z6
x4
z6
=
Information Theory for Data Management - Divesh & Suresh
Database Normal Forms
 Functional dependency: X → Y
–
Which design is better?
X
Y
Z
X
Y
X
Z
x1
y1
z1
x1
y1
x1
z1
x1
y1
z2
x2
y2
x1
z2
x2
y2
z3
x3
y3
x2
z3
x2
y2
z4
x4
y4
x2
z4
x3
y3
z5
x3
z5
x4
y4
z6
x4
z6
=
 Decomposition is in BCNF
110
Information Theory for Data Management - Divesh & Suresh
Database Normal Forms
 Multi-valued dependency: X →→ Y
–
111
Which design is better?
X
Y
Z
X
Y
X
Z
x1
y1
z1
x1
y1
x1
z1
x1
y2
z2
x1
y2
x1
z2
x1
y1
z2
x2
y3
x2
z3
x1
y2
z1
x3
y3
x2
z4
x2
y3
z3
x4
y3
x3
z5
x2
y3
z4
x4
z6
x3
y3
z5
x4
y3
z6
=
Information Theory for Data Management - Divesh & Suresh
Database Normal Forms
 Multi-valued dependency: X →→ Y
–
Which design is better?
X
Y
Z
X
Y
X
Z
x1
y1
z1
x1
y1
x1
z1
x1
y2
z2
x1
y2
x1
z2
x1
y1
z2
x2
y3
x2
z3
x1
y2
z1
x3
y3
x2
z4
x2
y3
z3
x4
y3
x3
z5
x2
y3
z4
x4
z6
x3
y3
z5
x4
y3
z6
=
 Decomposition is in 4NF
112
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Goal: use information theory to characterize “goodness” of a
database design and reason about normalization algorithms
 Key idea:
Information content measure of cell in a DB instance w.r.t. ICs
– Redundancy reduces information content measure of cells
–
 Results:
Well-designed DB  each cell has information content > 0
– Normalization algorithms never decrease information content
–
113
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Information content of cell c in database D satisfying FD X → Y
Uniform distribution p(V) on values for c consistent with D\c and FD
– Information content of cell c is entropy H(V)
–
X
Y
Z
V62 p(V62) h(V62)
x1
y1
z1
y1
0.25
2.0
x1
y1
z2
y2
0.25
2.0
x2
y2
z3
y3
0.25
2.0
x2
y2
z4
y4
0.25
2.0
x3
y3
z5
x4
y4
z6
 H(V62) = 2.0
114
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Information content of cell c in database D satisfying FD X → Y
Uniform distribution p(V) on values for c consistent with D\c and FD
– Information content of cell c is entropy H(V)
–
X
Y
Z
V22 p(V22) h(V22)
x1
y1
z1
y1
1.0
x1
y1
z2
y2
0.0
x2
y2
z3
y3
0.0
x2
y2
z4
y4
0.0
x3
y3
z5
x4
y4
z6
0.0
 H(V22) = 0.0
115
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Information content of cell c in database D satisfying FD X → Y
–
Information content of cell c is entropy H(V)
X
Y
Z
c
H(V)
x1
y1
z1
c12
0.0
x1
y1
z2
c22
0.0
x2
y2
z3
c32
0.0
x2
y2
z4
c42
0.0
x3
y3
z5
c52
2.0
x4
y4
z6
c62
2.0
 Schema S is in BCNF iff  D  S, H(V) > 0, for all cells c in D
–
116
Technicalities w.r.t. size of active domain
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Information content of cell c in database D satisfying FD X → Y
–
Information content of cell c is entropy H(V)
X
Y
X
Z
V12 p(V12) h(V12)
V42 p(V42) h(V42)
x1
y1
x1
z1
y1
0.25
2.0
y1
0.25
2.0
x2
y2
x1
z2
y2
0.25
2.0
y2
0.25
2.0
x3
y3
x2
z3
y3
0.25
2.0
y3
0.25
2.0
x4
y4
x2
z4
y4
0.25
2.0
y4
0.25
2.0
x3
z5
x4
z6
 H(V12) = 2.0, H(V42) = 2.0
117
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Information content of cell c in database D satisfying FD X → Y
–
Information content of cell c is entropy H(V)
X
Y
X
Z
c
H(V)
x1
y1
x1
z1
c12
2.0
x2
y2
x1
z2
c22
2.0
x3
y3
x2
z3
c32
2.0
x4
y4
x2
z4
c42
2.0
x3
z5
x4
z6
 Schema S is in BCNF iff  D  S, H(V) > 0, for all cells c in D
118
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Information content of cell c in DB D satisfying MVD X →→ Y
–
Information content of cell c is entropy H(V)
X
Y
Z
V52 p(V52) h(V52)
V53 p(V53) h(V53)
x1
y1
z1
y3
z1
0.2
2.32
x1
y2
z2
z2
0.2
2.32
x1
y1
z2
z3
0.2
2.32
x1
y2
z1
z4
0.0
x2
y3
z3
z5
0.2
2.32
x2
y3
z4
z6
0.2
2.32
x3
y3
z5
x4
y3
z6
1.0
0.0
 H(V52) = 0.0, H(V53) = 2.32
119
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Information content of cell c in DB D satisfying MVD X →→ Y
–
Information content of cell c is entropy H(V)
X
Y
Z
c
H(V)
c
H(V)
x1
y1
z1
c12
0.0
c13
0.0
x1
y2
z2
c22
0.0
c23
0.0
x1
y1
z2
c32
0.0
c33
0.0
x1
y2
z1
c42
0.0
c43
0.0
x2
y3
z3
c52
0.0
c53
2.32
x2
y3
z4
c62
0.0
c63
2.32
x3
y3
z5
c72
1.58
c73
2.58
x4
y3
z6
c82
1.58
c83
2.58
 Schema S is in 4NF iff  D  S, H(V) > 0, for all cells c in D
120
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Information content of cell c in DB D satisfying MVD X →→ Y
–
Information content of cell c is entropy H(V)
X
Y
X
Z
V32 p(V32) h(V32)
V34 p(V34) h(V34)
x1
y1
x1
z1
y1
0.33
1.58
z1
0.2
2.32
x1
y2
x1
z2
y2
0.33
1.58
z2
0.2
2.32
x2
y3
x2
z3
y3
0.33
1.58
z3
0.2
2.32
x3
y3
x2
z4
z4
0.0
x4
y3
x3
z5
z5
0.2
2.32
x4
z6
z6
0.2
2.32
 H(V32) = 1.58, H(V34) = 2.32
121
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Information content of cell c in DB D satisfying MVD X →→ Y
–
Information content of cell c is entropy H(V)
X
Y
X
Z
c
H(V)
c
H(V)
x1
y1
x1
z1
c12
1.0
c14
2.32
x1
y2
x1
z2
c22
1.0
c24
2.32
x2
y3
x2
z3
c32
1.58
c34
2.32
x3
y3
x2
z4
c42
1.58
c44
2.32
x4
y3
x3
z5
c52
1.58
c54
2.58
x4
z6
c64
2.58
 Schema S is in 4NF iff  D  S, H(V) > 0, for all cells c in D
122
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Normalization algorithms never decrease information content
–
123
Information content of cell c is entropy H(V)
X
Y
Z
c
H(V)
x1
y1
z1
c13
0.0
x1
y2
z2
c23
0.0
x1
y1
z2
c33
0.0
x1
y2
z1
c43
0.0
x2
y3
z3
c53
2.32
x2
y3
z4
c63
2.32
x3
y3
z5
c73
2.58
x4
y3
z6
c83
2.58
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Normalization algorithms never decrease information content
–
124
Information content of cell c is entropy H(V)
X
Y
Z
X
Y
X
Z
c
H(V)
c
H(V)
x1
y1
z1
x1
y1
x1
z1
c13
0.0
c14
2.32
x1
y2
z2
x1
y2
x1
z2
c23
0.0
c24
2.32
x1
y1
z2
x2
y3
x2
z3
c33
0.0
c34
2.32
x1
y2
z1
x3
y3
x2
z4
c43
0.0
c44
2.32
x2
y3
z3
x4
y3
x3
z5
c53
2.32
c54
2.58
x2
y3
z4
x4
z6
c63
2.32
c64
2.58
x3
y3
z5
c73
2.58
x4
y3
z6
c83
2.58
=
Information Theory for Data Management - Divesh & Suresh
Well-Designed Databases [AL03]
 Normalization algorithms never decrease information content
–
125
Information content of cell c is entropy H(V)
X
Y
Z
X
Y
X
Z
c
H(V)
c
H(V)
x1
y1
z1
x1
y1
x1
z1
c13
0.0
c14
2.32
x1
y2
z2
x1
y2
x1
z2
c23
0.0
c24
2.32
x1
y1
z2
x2
y3
x2
z3
c33
0.0
c34
2.32
x1
y2
z1
x3
y3
x2
z4
c43
0.0
c44
2.32
x2
y3
z3
x4
y3
x3
z5
c53
2.32
c54
2.58
x2
y3
z4
x4
z6
c63
2.32
c64
2.58
x3
y3
z5
c73
2.58
x4
y3
z6
c83
2.58
=
Information Theory for Data Management - Divesh & Suresh
Database Design: Summary
 Good database design essential for preserving data integrity
 Information theoretic measures useful for integrity constraints
FD X → Y holds iff InD measure H(Y|X) = 0
– MVD X →
→ Y holds iff H(Y,Z|X) = H(Y|X) + H(Z|X)
– Information theory to model correlations in specific database
–
 Information theoretic measures useful for normal forms
Schema S is in BCNF/4NF iff  D  S, H(V) > 0, for all cells c in D
– Information theory to model distributions over possible databases
–
126
Information Theory for Data Management - Divesh & Suresh
Outline
Part 1
 Introduction to Information Theory
 Application: Data Anonymization
 Application: Data Integration
Part 2




127
Review of Information Theory Basics
Application: Database Design
Computing Information Theoretic Primitives
Open Problems
Information Theory for Data Management - Divesh & Suresh
Domain size matters
 For random variable X, domain size = supp(X) = {xi | p(X = xi) >
0}
 Different solutions exist depending on whether domain size is
“small” or “large”
 Probability vectors usually very sparse
Entropy: Case I - Small domain size
 Suppose the #unique values for a random variable X is small
(i.e fits in memory)
 Maximum likelihood estimator:
–
p(x) = #times x is encountered/total number of items in set.
1
2
2
1
1 5
4
1
2
3
4
5
Entropy: Case I - Small domain size
 HMLE = Sx p(x) log 1/p(x)
 This is a biased estimate:
–
E[HMLE] < H
 Miller-Madow correction:
–
H’ = HMLE + (m’ – 1)/2n
 m’ is an estimate of number of non-empty bins
 n = number of samples
 Bad news: ALL estimators for H are biased.
 Good news: we can quantify bias and variance of MLE:
Bias <= log(1 + m/N)
– Var(HMLE) <= (log n)2/N
–
Entropy: Case II - Large domain size
 |X| is too large to fit in main memory, so we can’t maintain
explicit counts.
 Streaming algorithms for H(X):
Long history of work on this problem
– Bottomline:
(1+e)-relative-approximation for H(X) that allows for updates
to frequencies, and requires “almost constant”, and optimal
space [HNO08].
–
Streaming Entropy [CCM07]
 High level idea: sample randomly from the stream, and track
counts of elements picked [AMS]
 PROBLEM: skewed distribution prevents us from sampling
lower-frequency elements (and entropy is small)
 Idea: estimate largest frequency, and
distribution of what’s left (higher entropy)
Streaming Entropy [CCM07]
 Maintain set of samples from original distribution and
distribution without most frequent element.
 In parallel, maintain estimator for frequency of most frequent
element
normally this is hard
– but if frequency is very large, then simple estimator exists
[MG81] (Google interview puzzle!)
–
 At the end, compute function of these two estimates
 Memory usage: roughly 1/e2 log(1/e) (e is the error)
Entropy and MI are related
 I(X;Y) = H(X,Y) – H(X) – H(Y)
 Suppose we can c-approximate H(X) for any c > 0:
Find H’(X) s.t |H(X) – H’(X)| <= c
 Then we can 3c-approximate I(X;Y):
–
I(X;Y) = H(X,Y) – H(X) – H(Y)
<= H’(X,Y)+c – (H’(X)-c) – (H’(Y)-c)
<= H’(X,Y) – H’(X) – H’(Y) + 3c
<= I’(X,Y) + 3c
 Similarly, we can 2c-approximate H(Y|X) = H(X,Y) – H(X)
 Estimating entropy allows us to estimate I(X;Y) and H(Y|X)
Computing KL-divergence: Small Domains
 “easy algorithm”: maintain counts for each of p and q,
normalize, and compute KL-divergence.
 PROBLEM ! Suppose qi = 0:
–
pi log pi/qi is undefined !
 General problem with ML estimators: all events not seen have
probability zero !!
Laplace correction: add one to counts for each seen element
– Slightly better: add 0.5 to counts for each seen element [KT81]
– Even better, more involved: use Good-Turing estimator [GT53]
–
 YIeld non-zero probability for “things not seen”.
Computing KL-divergence: Large Domains
 Bad news: No good relative-approximations exist in small
space.
 (Partial) good news: additive approximations in small space
under certain technical conditions (no pi is too small).
 (Partial) good news: additive approximations for symmetric
variant of KL-divergence, via sampling.
 For details, see [GMV08,GIM08]
Information-theoretic Clustering
 Given a collection of random variables X, each “explained” by
a random variable Y, we wish to find a (hard or soft) clustering
T such that
I(T,X) – bI(T, Y)
is minimized.
 Features of solutions thus far:
heuristic (general problem is NP-hard)
– address both small-domain and large-domain scenarios.
–
Agglomerative Clustering (aIB) [ST00]
 Fix number of clusters k
1. While number of clusters < k
Determine two clusters whose merge loses the least
information
2. Combine these two clusters
1.
2. Output clustering
 Merge Criterion:
–
merge the two clusters so that change in I(T;V) is minimized
 Note: no consideration of b (number of clusters is fixed)
Agglomerative Clustering (aIB) [S]
 Elegant way of finding the two clusters to be merged:
 Let dJS(p,q) = (1/2)(dKL(p,m) + dKL(q,m)), m = (p+q)/2
p
m
q
 dJS(p,q) is a symmetric distance between p, q (JensenShannon distance)
 We merge clusters that have smallest dJS(p,q), (weighted by
cluster mass)
Iterative Information Bottleneck (iIB) [S]
 aIB yields a hard clustering with k clusters.
 If you want a soft clustering, use iIB (variant of EM)
Step 1: p(t|x) ← exp(-bdKL(p(V|x),p(V|t))
 assign elements to clusters in proportion (exponentially) to
distance from cluster center !
– Step 2: Compute new cluster centers by computing weighted
centroids:
 p(t) = Sx p(t|x) p(x)
 p(V|t) = Sx p(V|t) p(t|x) p(x)/p(t)
– Choose b according to [DKOSV06]
–
Dealing with massive data sets
 Clustering on massive data sets is a problem
 Two main heuristics:
Sampling [DKOSV06]:
 pick a small sample of the data, cluster it, and (if necessary)
assign remaining points to clusters using soft assignment.
 How many points to sample to get good bounds ?
– Streaming:
 Scan the data in one pass, performing clustering on the fly
 How much memory needed to get reasonable quality
solution ?
–
LIMBO (for aIB) [ATMS04]
 BIRCH-like idea:
Maintain (sparse) summary for each cluster (p(t), p(V|t))
– As data streams in, build clusters on groups of objects
– Build next-level clusters on cluster summaries from lower level
–
Outline
Part 1
 Introduction to Information Theory
 Application: Data Anonymization
 Application: Data Integration
Part 2




143
Review of Information Theory Basics
Application: Database Design
Computing Information Theoretic Primitives
Open Problems
Information Theory for Data Management - Divesh & Suresh
Open Problems
 Data exploration and mining – information theory as first-pass
filter
 Relation to nonparametric generative models in machine
learning (LDA, PPCA, ...)
 Engineering and stability: finding right knobs to make systems
reliable and scalable
 Other information-theoretic concepts ? (rate distortion,
higher-order entropy, ...)
THANK YOU !
References: Information Theory
 [CT] Tom Cover and Joy Thomas: Information Theory.
 [BMDG05] Arindam Banerjee, Srujana Merugu, Inderjit Dhillon, Joydeep Ghosh.
Learning with Bregman Divergences, JMLR 2005.
 [TPB98] Naftali Tishby, Fernando Pereira, William Bialek. The Information Bottleneck
Method. Proc. 37th Annual Allerton Conference, 1998
145
Information Theory for Data Management - Divesh & Suresh
References: Data Anonymization
 [AA01] Dakshi Agrawal, Charu C. Aggarwal: On the design and quantification of privacy
preserving data mining algorithms. PODS 2001.
 [AS00] Rakesh Agrawal, Ramakrishnan Srikant: Privacy preserving data mining. SIGMOD
2000.
 [EGS03] Alexandre Evfimievski, Johannes Gehrke, Ramakrishnan Srikant: Limiting
privacy breaches in privacy preserving data mining. PODS 2003.
146
Information Theory for Data Management - Divesh & Suresh
References: Data Integration
 [AMT04] Periklis Andritsos, Renee J. Miller, Panayiotis Tsaparas: Information-theoretic
tools for mining database structure from large data sets. SIGMOD 2004.
 [DKOSV06] Bing Tian Dai, Nick Koudas, Beng Chin Ooi, Divesh Srivastava, Suresh
Venkatasubramanian: Rapid identification of column heterogeneity. ICDM 2006.
 [DKSTV08] Bing Tian Dai, Nick Koudas, Divesh Srivastava, Anthony K. H. Tung, Suresh
Venkatasubramanian: Validating multi-column schema matchings by type. ICDE 2008.
 [KN03] Jaewoo Kang, Jeffrey F. Naughton: On schema matching with opaque column
names and data values. SIGMOD 2003.
 [PPH05] Patrick Pantel, Andrew Philpot, Eduard Hovy: An information theoretic model
for database alignment. SSDBM 2005.
147
Information Theory for Data Management - Divesh & Suresh
References: Database Design
 [AL03] Marcelo Arenas, Leonid Libkin: An information theoretic approach to normal
forms for relational and XML data. PODS 2003.
 [AL05] Marcelo Arenas, Leonid Libkin: An information theoretic approach to normal
forms for relational and XML data. JACM 52(2), 246-283, 2005.
 [DR00] Mehmet M. Dalkilic, Edward L. Robertson: Information dependencies. PODS
2000.
 [KL06] Solmaz Kolahi, Leonid Libkin: On redundancy vs dependency preservation in
normalization: an information-theoretic study of XML. PODS 2006.
148
Information Theory for Data Management - Divesh & Suresh
References: Computing IT quantities
 [P03] Liam Panninski. Estimation of entropy and mutual information. Neural
Computation 15: 1191-1254
 [GT53] I. J. Good. Turing’s anticipation of Empirical Bayes in connection with the
cryptanalysis of the Naval Enigma. Journal of Statistical Computation and Simulation,
66(2), 2000.
 [KT81] R. E. Krichevsky and V. K. Trofimov. The performance of universal encoding. IEEE
Trans. Inform. Th. 27 (1981), 199--207.
 [CCM07] Amit Chakrabarti, Graham Cormode and Andrew McGregor. A near-optimal
algorithm for computing the entropy of a stream. Proc. SODA 2007.
 [HNO] Nich Harvey, Jelani Nelson, Krzysztof Onak. Sketching and Streaming Entropy via
Approximation Theory. FOCS 2008
 [ATMS04] Periklis Andritsos, Panayiotis Tsaparas, Renée J. Miller and Kenneth C. Sevcik.
LIMBO: Scalable Clustering of Categorical Data. EDBT 2004
149
Information Theory for Data Management - Divesh & Suresh
References: Computing IT quantities
 [S] Noam Slonim. The Information Bottleneck: theory and applications. Ph.D Thesis.
Hebrew University, 2000.
 [GMV08] Sudipto Guha, Andrew McGregor, Suresh Venkatasubramanian. Streaming
and sublinear approximations for information distances. ACM Trans Alg. 2008
 [GIM08] Sudipto Guha, Piotr Indyk, Andrew McGregor. Sketching Information
Distances. JMLR, 2008.
150
Information Theory for Data Management - Divesh & Suresh