Big Data Becomes Personal: Knowledge into Meaning: Papers from the AAAI Spring Symposium
Personal Life Repository as a Distributed PDS
and Its Dissemination Strategy for Healthcare Services
Kôiti Hasida
Social ICT Research Center, Graduate School of Information Science and Technology, the University of Tokyo
hasida.koiti@i.u-tokyo.ac.jp
Abstract
As a distributed PDS (personal data store), PLR (personal
life repository) allows individual users to totally control
their own data and drastically reduces the cost and risk to
service providers in storing personal data. Using basically
free cloud storage services for sharing data, PLR is also
easy to deploy, inexpensive to operate, and hence smooth to
disseminate. PLR can spread through collaboration with
existing service providers.
Personal Life Repository
PDS (personal data store) is an IT tool to allow individual
users to accumulate and maintain their own data and to
socially share the data while explicitly specifying which
data to share with whom. Shown in Figure 1, PLR
(personal life repository; Hasida, 2012; 2103) is a kind of
distributed PDS.
Figure 2: Present Architecture of PLR
PLR encrypts personal data in such a way that each PLR
user herself and those whom she explicitly permits to
access the data can decrypt the data but others including
the storage service providers cannot.
The fundamental functionalities of PDS for both data
storage and sharing are provided by existing cloud storage
services basically for free. One could alternatively use
home servers or PaaS- or IaaS-type cloud services such as
Google App Engine and Amazon EC2, but they offer
rather limited free storage and an additional service is
necessary in order to provide common IDs for
authenticating accesses to shared data. Existing IDprovision services such as those of Facebook and Twitter
may be employed here. However, the current PLR
implementation instead utilizes cloud storage services
integrating data storage and sharing, which is more
convenient particularly for spreading PLR all over the
world including those areas where those popular IDprovision services are unavailable.
Figure 1: Functionalities of PLR
It is currently designed to make full use of existing
commodities. In particular, it utilizes personal cloud
storage services such as Google Drive and Dropbox (or
networks of cloud storage services, such as Respect
Network) as shown in Figure 2, for the sake of sharing data
among PLR users.
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Data Aggregation
sharing the care records with the residents’ family
members. As many nursing homes are affiliated with
hospitals, data sharing with those hospitals is beneficial to
the nursing homes and hence to the hospitals.
Second, some EHR (electronic health record) venders
may well be interested in providing hospitals with the
functionality of EHR for data coordination with PLR,
which will benefit both the vendors and the hospitals. The
venders and third parties will be able to provide
commercial health-management services to patients of
those hospitals. That will reduce the times and the
durations of hospitalizations, and hence the hospitals’
expenditure.
Either way, healthcare stakeholders will be able to share
data as shown in Figure 4.
One must physically aggregate personal data from many
people’s PLR in order to efficiently analyze the big
personal data. Of course PLR itself supports this
aggregation, but one would use larger-scale servers to
aggregate huge amount of data. Figure 3 shows a privacypreserving data aggregation, where the data broker collects
personal data from many PLR users, let the analyst
manipulate the data, and give him the analysis result,
without disclosing raw data to him.
Figure 3: Aggregation of Distributed Big Data
The data broker can also conceal the analysis result if it
contains any information possibly violating somebody’s
privacy. This scheme of data aggregation and utilization
both protects privacy and allows individuals to easily keep
track of and control how their personal data are used. Note
that it also promotes utilization of personal data, because
PLR makes it very easy for the data broker to ask
individuals to disclose their data.
Figure 4: Healthcare Data Coordination
Final Remarks
PLR, OpenPDS (de Montjoye, et al., 2012), and the other
PDSs should be interoperable. As they spread, PaaS-type
cloud services to support data storage and sharing are
expected to appear and facilitate the implementation and
operation of distributed PDSs.
Dissemination Strategy
Typically expected usages of PDS are those in healthcare
services, where PDS serves as PHR (personal health
record). PHR has been successful only through top-down
dissemination in some European countries, but privatesector PHR, such as Google Health, Microsoft HealthVault,
and My Hospital Everywhere (in Japan), are far from being
popular. This is probably because medical records have
been accumulated and stored mainly at hospitals but they
have not directly benefited from sharing the data.
There are some dissemination strategies for PLR-based
healthcare entailing profits of all stakeholders including
hospitals, which are effective at least in Japan.
First, most elderly nursing homes have not yet been
computerized in Japan. The introduction of PLR into
nursing homes will not only reduce the workload of
caregivers but also create a new profitable service of
References
Kôiti Hasida. 2012. Personal Life Repository as Foundation of
B2C Services ― Big Data without Big Brother ―. The 21st
Annual Frontiers in Service Conference, Maryland University.
Kôiti Hasida. 2013. Personal Life Repository for ConsumerInitiative Services. The 22nd Annual Frontiers in Service
Conference, 08-102, National Taiwan University.
Yves-Alexandre de Montjoye, Samuel S. Wang, Alex (Sandy)
Pentland. 2012. On the Trusted Use of Large-Scale Personal
Data. Bulletin of the Technical Committee on Data Engineering,
35(4), 5-8.
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