21,824 research outputs found
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of Location-Based Services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to Location-Based Services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users
Shortest Path Computation with No Information Leakage
Shortest path computation is one of the most common queries in location-based
services (LBSs). Although particularly useful, such queries raise serious
privacy concerns. Exposing to a (potentially untrusted) LBS the client's
position and her destination may reveal personal information, such as social
habits, health condition, shopping preferences, lifestyle choices, etc. The
only existing method for privacy-preserving shortest path computation follows
the obfuscation paradigm; it prevents the LBS from inferring the source and
destination of the query with a probability higher than a threshold. This
implies, however, that the LBS still deduces some information (albeit not
exact) about the client's location and her destination. In this paper we aim at
strong privacy, where the adversary learns nothing about the shortest path
query. We achieve this via established private information retrieval
techniques, which we treat as black-box building blocks. Experiments on real,
large-scale road networks assess the practicality of our schemes.Comment: VLDB201
DIAMOnDS - DIstributed Agents for MObile & Dynamic Services
Distributed Services Architecture with support for mobile agents between
services, offer significantly improved communication and computational
flexibility. The uses of agents allow execution of complex operations that
involve large amounts of data to be processed effectively using distributed
resources. The prototype system Distributed Agents for Mobile and Dynamic
Services (DIAMOnDS), allows a service to send agents on its behalf, to other
services, to perform data manipulation and processing. Agents have been
implemented as mobile services that are discovered using the Jini Lookup
mechanism and used by other services for task management and communication.
Agents provide proxies for interaction with other services as well as specific
GUI to monitor and control the agent activity. Thus agents acting on behalf of
one service cooperate with other services to carry out a job, providing
inter-operation of loosely coupled services in a semi-autonomous way. Remote
file system access functionality has been incorporated by the agent framework
and allows services to dynamically share and browse the file system resources
of hosts, running the services. Generic database access functionality has been
implemented in the mobile agent framework that allows performing complex data
mining and processing operations efficiently in distributed system. A basic
data searching agent is also implemented that performs a query based search in
a file system. The testing of the framework was carried out on WAN by moving
Connectivity Test agents between AgentStations in CERN, Switzerland and NUST,
Pakistan.Comment: 7 pages, 4 figures, CHEP03, La Jolla, California, March 24-28, 200
Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation
With the wide deployment of public cloud computing infrastructures, using
clouds to host data query services has become an appealing solution for the
advantages on scalability and cost-saving. However, some data might be
sensitive that the data owner does not want to move to the cloud unless the
data confidentiality and query privacy are guaranteed. On the other hand, a
secured query service should still provide efficient query processing and
significantly reduce the in-house workload to fully realize the benefits of
cloud computing. We propose the RASP data perturbation method to provide secure
and efficient range query and kNN query services for protected data in the
cloud. The RASP data perturbation method combines order preserving encryption,
dimensionality expansion, random noise injection, and random projection, to
provide strong resilience to attacks on the perturbed data and queries. It also
preserves multidimensional ranges, which allows existing indexing techniques to
be applied to speedup range query processing. The kNN-R algorithm is designed
to work with the RASP range query algorithm to process the kNN queries. We have
carefully analyzed the attacks on data and queries under a precisely defined
threat model and realistic security assumptions. Extensive experiments have
been conducted to show the advantages of this approach on efficiency and
security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201
- …