dc.contributor.authorYuan, Ganzhao
dc.contributor.authorZhang, Zhenjie
dc.contributor.authorWinslett, Marianne
dc.contributor.authorXiao, Xiaokui
dc.contributor.authorYang, Yin
dc.contributor.authorHao, Zhifeng
dc.date.accessioned2017-07-27T08:09:21Z
dc.date.available2017-07-27T08:09:21Z
dc.date.issued2015
dc.identifier.citationYuan, G., Zhang, Z., Winslett, M., Xiao, X., Yang, Y., & Hao, Z. (2015). Optimizing Batch Linear Queries under Exact and Approximate Differential Privacy. ACM Transactions on Database Systems, 40(2), 11-.en_US
dc.identifier.issn0362-5915en_US
dc.identifier.urihttp://hdl.handle.net/10220/43472
dc.description.abstractDifferential privacy is a promising privacy-preserving paradigm for statistical query processing over sensitive data. It works by injecting random noise into each query result such that it is provably hard for the adversary to infer the presence or absence of any individual record from the published noisy results. The main objective in differentially private query processing is to maximize the accuracy of the query results while satisfying the privacy guarantees. Previous work, notably Li et al. [2010], has suggested that, with an appropriate strategy, processing a batch of correlated queries as a whole achieves considerably higher accuracy than answering them individually. However, to our knowledge there is currently no practical solution to find such a strategy for an arbitrary query batch; existing methods either return strategies of poor quality (often worse than naive methods) or require prohibitively expensive computations for even moderately large domains. Motivated by this, we propose a low-rank mechanism (LRM), the first practical differentially private technique for answering batch linear queries with high accuracy. LRM works for both exact (i.e., ε-) and approximate (i.e., (ε, δ)-) differential privacy definitions. We derive the utility guarantees of LRM and provide guidance on how to set the privacy parameters, given the user's utility expectation. Extensive experiments using real data demonstrate that our proposed method consistently outperforms state-of-the-art query processing solutions under differential privacy, by large margins.en_US
dc.description.sponsorshipMOE (Min. of Education, S’pore)en_US
dc.format.extent45 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesACM Transactions on Database Systemsen_US
dc.rights© 2015 ACM. This is the author created version of a work that has been peer reviewed and accepted for publication by ACM Transactions on Database Systems, ACM. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1145/2699501].en_US
dc.subjectLinear counting queryen_US
dc.subjectDifferential privacyen_US
dc.titleOptimizing Batch Linear Queries under Exact and Approximate Differential Privacyen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1145/2699501
dc.description.versionAccepted versionen_US


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