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Title: Privacy Streamliner: A Two-Stage Approach to Improving Algorithm Efficiency

Citation Type: Miscellaneous

Publication Year: 2012

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Abstract: In releasing data with sensitive information, a data owner usually has seemingly conflicting goals, including privacy preservation, util- ity optimization, and algorithm efficiency. In this paper, we observe that a high computational complexity is usually incurred when an algorithm conflates the processes of privacy preservation and util- ity optimization. We then propose a novel privacy streamliner ap- proach to decouple those two processes for improving algorithm efficiency. More specifically, we first identify a set of potential privacy-preserving solutions satisfying that an adversary’s knowl- edge about this set itself will not help him/her to violate the pri- vacy property; we can then optimize utility within this set without worrying about privacy breaches since such an optimization is now simulatable by adversaries. To make our approach more concrete, we study it in the context of micro-data release with publicly known generalization algorithms. The analysis and experiments both con- firm our algorithms to be more efficient than existing solutions.

Url: https://users.encs.concordia.ca/~wang/papers/codaspy12.pdf

User Submitted?: No

Authors: Liu, Wen, M; Wang, Lingyu

Publisher: Concordia Institute for Information Systems Engineering

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: United States