Abstract:
Participatory sensing technology is designed to facilitate community people collect, analyze,
and share information for their mutual bene t in a cost-e ective way using smart-phones,
camera or other ad-hoc sensing devices. The apparently insensitive information transmitted
in plaintext through a lightweight infrastructure of participatory sensing system can be
used by an eavesdropper to infer some sensitive information and threaten the privacy of
the observer. Su cient number of participants is imperative for the success of participatory
sensing. Participation depends a great deal on the assurance of privacy protection. Existing
techniques add some uncertainty to the actual observation to achieve anonymity of the
participants which, however, diminishes data integrity to an unacceptable extent. A subsetcoding
based anonymization technique was proposed in [1] to safeguard observers' location
privacy from adversaries while preserving almost loss-less data integrity at the destination
server. However, the high computational complexity of that technique O(N!) allowed its use
at limited level. In this thesis, we develop an O(N) technique to overcome this limitation.
The new technique accommodate variable degree of desired anonymization for the users
which eventually enables designing
exible incentive schemes for the users. Finally, to the
best of our knowledge, we present the rst multi-dimensional privacy preserving scheme
that can protect users' privacy at di erent dimensions simultaneously. For example, both
location and product association of an observer can be protected. Comprehensive simulation
and Android prototype based experiments are carried out to establish the applicability of
the proposed schemes.
