The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Protecting Location Privacy Through Path Confusion
Abstract
We present a path perturbation algorithm which can maximize users’ location privacy given a quality of service constraint. This work concentrates on a class of applications that continuously collect location samples from a large group of users, where just removing user identifiers from all samples is insufficient because an adversary could use trajectory information to track paths and follow users’ footsteps home. The key idea underlying the perturbation algorithm is to cross paths in areas where at least two users meet. This increases the chances that an adversary would confuse the paths of different users. We first formulate this privacy problem as a constrained optimization problem and then develop heuristics for an efficient privacy algorithm. Using simulations with randomized movement models we verify that the algorithm improves privacy while minimizing the perturbation of location samples.
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Citations
Cites
k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571
An algorithm for tracking multiple targets
Reid, D. (1979) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1979.1102177 · Cited by 3,059
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335438 · Cited by 2,961
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Gruteser, M., & Grunwald, D. (2003) · Proceedings of the 1st international conference on Mobile systems, applications and services · doi:10.1145/1066116.1189037 · Cited by 2,275
Self-citationACHIEVING k-ANONYMITY PRIVACY PROTECTION USING GENERALIZATION AND SUPPRESSION
SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s021848850200165x · Cited by 1,976
Privacy-preserving data mining
Agrawal, R., & Srikant, R. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335438 · Cited by 1,719
Location privacy in pervasive computing
Beresford, A. R., & Stajano, F. (2003) · IEEE Pervasive Computing · doi:10.1109/mprv.2003.1186725 · Cited by 1,568
On the design and quantification of privacy preserving data mining algorithms
Agrawal, D., & Aggarwal, C. C. (2001) · Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/375551.375602 · Cited by 1,006
Díaz, C., Seys, S., Claessens, J., & Preneel, B. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-36467-6_5 · Cited by 728
Towards an Information Theoretic Metric for Anonymity
Serjantov, A., & Danezis, G. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-36467-6_4 · Cited by 663
On the privacy preserving properties of random data perturbation techniques
Kargupta, H., Datta, S., Wang, Q., & Sivakumar, K. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250908 · Cited by 618
Mix zones: user privacy in location-aware services
Beresford, A. R., & Stajano, F. (2004) · IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second · doi:10.1109/percomw.2004.1276918 · Cited by 485
A Privacy Awareness System for Ubiquitous Computing Environments
Langheinrich, M. (2002) · Cited by 430
Preserving privacy in environments with location-based applications
Myles, G., Friday, A., & Davies, N. (2003) · IEEE Pervasive Computing · doi:10.1109/mprv.2003.1186726 · Cited by 361
Toward Privacy in Public Databases
Chawla, S., Dwork, C., McSherry, F., Smith, A., & Wee, H. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30576-7_20 · Cited by 264
Enhancing wireless location privacy using silent period
Huang, L., Matsuura, K., Yamane, H., & Sezaki, K. (2005) · IEEE Wireless Communications and Networking Conference, 2005 · doi:10.1109/wcnc.2005.1424677 · Cited by 259
Random-data perturbation techniques and privacy-preserving data mining
Kargupta, H., Datta, S., Wang, Q., & Sivakumar, K. (2005) · Knowledge and Information Systems · doi:10.1007/s10115-004-0173-6 · Cited by 212
Framework for security and privacy in automotive telematics
Duri, S., Gruteser, M., Liu, X., Moskowitz, P., Perez, R., Singh, M., & Tang, J.-M. (2002) · Proceedings of the 2nd international workshop on Mobile commerce · doi:10.1145/570705.570711 · Cited by 173
Self-citationOn the Anonymity of Periodic Location Samples
Gruteser, M., & Hoh, B. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-32004-3_19 · Cited by 160
Self-citation
Cited by
Shokri, R., Theodorakopoulos, G., Le Boudec, J.-Y., & Hubaux, J.-P. (2011) · 2011 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2011.18 · Cited by 699
Pseudonym Schemes in Vehicular Networks: A Survey
Petit, J., Schaub, F., Feiri, M., & Kargl, F. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2345420 · Cited by 434
Technical Privacy Metrics: A Systematic Survey
Wagner, I., & Eckhoff, D. (2019) · ACM Computing Surveys · doi:10.1145/3168389 · Cited by 346
Broadening the Scope of Differential Privacy Using Metrics
Chatzikokolakis, K., Andrés, M. E., Bordenabe, N. E., & Palamidessi, C. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-39077-7_5 · Cited by 340
Privacy in mobile technology for personal healthcare
Avancha, S., Baxi, A., & Kotz, D. (2012) · ACM Computing Surveys · doi:10.1145/2379776.2379779 · Cited by 229
Metadata
- Authors
- Hoh, B., & Gruteser, M.
- Venue
- First International Conference on Security and Privacy for Emerging Areas in Communications Networks (SECURECOMM'05)
- Publisher
- IEEE
- Published
- 2006-03-22
- Pages
- 194-205
- OpenAlex
- W2125613938
- Cited by
- 386
- References
- 19
- FWCI
- 18.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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