Nonlinear programming: theory, algorithms, and applications
Nonlinear programming: theory, algorithms, and applications
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
On the design and quantification of privacy preserving data mining algorithms
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Location Privacy in Pervasive Computing
IEEE Pervasive Computing
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Mobility modelling and trajectory prediction for cellular networks with mobile base stations
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Enhancing Source-Location Privacy in Sensor Network Routing
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
Developing privacy guidelines for social location disclosure applications and services
SOUPS '05 Proceedings of the 2005 symposium on Usable privacy and security
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
From data privacy to location privacy: models and algorithms
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
User-Controllable Security and Privacy for Pervasive Computing
HOTMOBILE '07 Proceedings of the Eighth IEEE Workshop on Mobile Computing Systems and Applications
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
IEEE Transactions on Mobile Computing
Privacy-aware routing in sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
A family of enhanced (L,α)-diversity models for privacy preserving data publishing
Future Generation Computer Systems
A distributed architecture of Sensing Web for sharing open sensor nodes
Future Generation Computer Systems
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
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With the pervasive penetration of the sensor networks into people's daily life, data are becoming easily obtainable. While the information is useful in many aspects, personal privacy is greatly challenged too. In this paper, we are interested in the applications where the sensor networks are deployed to monitor the locations of a person (or an animal). While the location information is useful for the interested public or scientists, we found that a detailed knowledge of the past behavior and current track of the person can disclose his future locations; which may bring in privacy or security concerns. We call this a successive privacy problem. Notice that this is in sharp contrast to previous location privacy studies which tries to mask, through K-anonymity, an individual past or current location of a person. To date, given a sequence of past observations, abundant techniques are available to infer future locations of an object. We observe that intrinsically, each observation will contribute to the inference accuracy. Therefore, in this paper, we generalize it into a weighted representation. That is, the observations are associated with weights which show the (joint) impact on releasing the observations to inference of future data. We observed that there is an intrinsic trade-off between the number of data to be published to the interested parties and the privacy preservation of the object. We show that the problem can be formulated into a non-linear optimization problem. As the problem is intractable, we develop optimal solutions to some special cases through dynamic programming and several heuristics for the general case. We then show several privacy aware data collection schemes; their performance and efficiency. Extensive simulations demonstrate the effectiveness of our schemes.