PARROTS
Position Altered Random Repetition
of Transportation Signature
George Corser
Oakland University
May 23, 2013
1. Introduction: VANET Privacy
3
VANET Privacy: Scope
Virtue we wish
to promote
Scope
Privacy
Activity we wish
to control
Vehicle
Surveillance
System possible
to implement
Mobile
Wireless
Network
VANET: Technical Standards
• Two stacks
▫ WSMP (Safety)
▫ TCP/IP (Other)
• IEEE 1609.2
▫ Security Services
• J2735
▫ DSRC Message Set
• J2945.1
▫ Comm. Performance
(Graphic source: Kenney, 2010)
First non-draft version
released April 2013
IEEE 1609.2 – Security Services
• Certificates and Certificate Authority Hierarchy
• Formats for Public Key, Signature, Certificate,
and CRL
• Message Formats and Processing for Generating
Encrypted Messages
• Sending Messages
Jared: Trust
George: Privacy
• Request Certificates from the CA
• Request and Processing CRL
Source: IEEE 1609.2 – Security Services
SAE J2735 – DSRC Message Set
• Basic Safety Message (BSM)
• Probe Vehicle Data Message (PVDM)
• Traveler Information (TIM)
Image source: http://www.sae.org/exempt/misc/dsrc/docs.htm#complexType_TravelerInformation_Link0780A7A0
Crypto validates identities and
keeps messages confidential
Basic Crypto: Vi queries LBS
• i = identity (pseudo identity, actually)
• Vi = vehicle with identity, i
• Cert(i) = CA-(i, Vi+, validity, authority, …)
▫ the digital certificate for Vi is the identity, the
public key and the key’s valid date/time range
•
•
•
•
q = query (could have used: m = message)
SigVi(q) = V-[H(q),q]
Query Vi →LBS (q) = LBS+[ SigVi(q) , Cert(i) ]
Reply LBS→Vi (r) = Vi+[r]
VANET Privacy:
Properties, Techniques, Problems
• Unlinkability, pseudo IDs, key management
• Untrackability, synchronized pseudo ID
change, sparsity/density of vehicle traffic
• Scalability, no solution, sparsity/density
(unpublished: FLARES addresses this issue)
• Efficiency, minimize privacy message requests,
efficient protocols not usually as effective
• Conditionality, distributed PKI, tends to work
against unlinkability
VANET Privacy: PARROTS
Properties, Techniques, Problems
• User choice, PARROTS, driver-controlled
privacy may circumvent conditionality (note:
other models could offer a software switch, but
none in the literature have so suggested)
• Defense against collaboration, PARROTS,
requires different BSM, PVM, GSM pseudo IDs
PARROTS: Contribution of Paper
• Model for defending
against collaborative
location privacy attacks
in VANETs
2. PARROTS
Threat Model: Collaborative Attack
Attacker has access
to both LBS and RSU
Many models use this technique
Privacy Technique: Pseudo-ID
• Real identity never broadcast
▫ Ensures unlinkability
• Pseudo ID changes every 5 minutes
• Key distribution and certificate
revocation also open research issues
Certificate
Authority
Many models use this technique
Privacy Technique: Group Leader
• Vehicles travel in groups
▫ Ensures untrackability
• Groups serve as mix zones
• Followers synchronize pseudo ID changes (may
also use silent period)
• Group leader does not have privacy
Original work
PARROTS: Before Group Change
Vi
Vi and Vj drive within
communication range,
Vj agrees to PARROT
Vj
Original work
PARROTS: After Group Change
Vi
Vj
Vi
Vj changes group and
begins parroting Vi
PARROTS: Defeats Attack
Both Vi and Vj send LBS requests signed by
Vi, and both locations confirmed by RSUs
Vj
Vj
Vi
Vi
Problems with PARROTS Model
• Vehicles would need separate sets of pseudo IDs
for safety applications. The pseudo ID for the
BSM cannot be the same as the pseudo ID for
the TIM (or whatever message type is used for
the LBS) otherwise attacker could check for BSM
• Parrotee would need to construct maybe 5 mins
worth of messages to send to LBS
• Location cannot be part of signed request
• Malicious parroter could flood LBS
Crypto validates identities and
keeps messages confidential
Basic Crypto: Vi queries LBS
• i = identity (pseudo identity, actually)
• Vi = vehicle with identity, i
• Cert(i) = CA-(i, Vi+, validity, authority, …)
▫ the digital certificate for Vi is the identity, the
public key and the key’s valid date/time range
•
•
•
•
q = query (could have used: m = message)
SigVi(q) = V-[H(q),q]
Query Vi →LBS (q) = LBS+[ SigVi(q) , Cert(i) ]
Reply LBS→Vi (r) = Vi+[r]
Conditionality Problem
• PARROTS increases the complexity of
conditional privacy. If LBS kept records of all
requests from Vi, and RSUs kept records of all
pseudo IDs of BSMs in range of RSU, then the
CA could correlate pseudo IDs to identify which
was the “real” request (assuming no spoofing).
3. Simulation
Image source: http://vc.inf.h-bonn-rhein-sieg.de/?page_id=1025
Mobility Model: Manhattan
•
•
•
•
3000 m by 3000 m
Roads every 100 m
All vehicles are on roads
Vehicle communication range
300 m
• Cars travel average 30 m/sec
Privacy Metrics
• Anonymity set size:
▫ |ASi|
• Entropy of anonymity set size:
▫ H(|ASi|) = Σ p(i,j) log2p(i,j)
• Tracking probability:
▫ Prob(|ASi| = 1)
Simulation: Python 2.7 Code
#
#
#
#
#
#
#
#
#
---------------------------------------------------------------------parrots.py
George Corser, January 28, 2013
Simulation of PARROTS, a VANET privacy model, wirtten in Python 2.7
PARROTS: Position Altered Random Repetition of Transporation Signature
See the "Main" section at the bottom of this file to change parameters.
This simulation assumes a grid of roads 100m apart on a 3000mx3000m area
----------------------------------------------------------------------
def PARROTS(t, v, parrotee_percent, parroter_percent, seednum):
# Function arguments ---------------------------------------------# t is number of time slices. Each time slice is: comfreq = 300 ms
# v is number of vehicles in simulation
# parrotee_percent is the ratio of vehicles that wish to request parroting
# parroter_percent is the ratio of vehicles that volunteer to be parrots
# seednum is seed in random.seed(seednum) for random.randint()
continued…
continued
Simulation: Python 2.7 Code
# ------------------------------------------------------------------------ #
# Step 1.a. Initialize vehicle locations
# ------------------------------------------------------------------------ #
for ti in range(1): # initialize vehicles at random coordinates on road grid
for vi in range(v):
# ----- Vehicles, Groups and Leaders ----------------------------- #
if vi % 2 == 0: # if vi is even, let x be an even 100 and y be random
x.append(100*random.randint(0,xmax/100))
y.append(random.randint(0,ymax))
else:
x.append(random.randint(0,xmax))
y.append(100*random.randint(0,ymax/100))
xprior.append(0)
yprior.append(0)
xdir.append((-1)**random.randint(1,2)) # randomly select -1 or 1
ydir.append((-1)**random.randint(1,2))
continued…
PARROT-ing almost doubles AS size
Simulation: Output
ti
600
600
600
600
600
600
600
600
vi
100
100
100
100
200
200
200
200
Sum
Sum Sum Sum Count
(as) pep prp (pe) (pr) (pas) (pas)
132
0
0
0
0
0
0
132
0 100
0
98
0
0
132 100
0
99
0
0
0
132 100 100
99
98 103
73
398
0
0
0
0
0
0
398
0 100
0 195
0
0
398 100
0
197
0
0
0
398 100 100
197 195 364 170
Average Parrotee Anonymity Set Sizes
Based on Parroter Percentage after 5 mins
Simulation: Graphed Output
Problems with simulation
• In simulation, all cars are on road. In real life
cars would be in parking lots, driveways, alleys,
and other places that are not roads.
• In simulation, when cars reach edge of grid they
turn around and go back into the grid. In real life
they would leave the grid area and perhaps new
cars would enter.
• In simulation, cars are uniformly distributed. In
real life they are concentrated in certain spots.
4. Conclusion
1. VANET Privacy
2. PARROTS Model
3. Simulation