The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Smooth sensitivity and sampling in private data analysis
Abstract
We introduce a new, generic framework for private data analysis.The goal of private data analysis is to release aggregate information about a data set while protecting the privacy of the individuals whose information the data set contains.Our framework allows one to release functions f of the data withinstance-based additive noise. That is, the noise magnitude is determined not only by the function we want to release, but also bythe database itself. One of the challenges is to ensure that the noise magnitude does not leak information about the database. To address that, we calibrate the noise magnitude to the smoothsensitivity of f on the database x --- a measure of variabilityof f in the neighborhood of the instance x. The new frameworkgreatly expands the applicability of output perturbation, a technique for protecting individuals' privacy by adding a smallamount of random noise to the released statistics. To our knowledge, this is the first formal analysis of the effect of instance-basednoise in the context of data privacy.
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Citations
Cites
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
Self-citationOur Data, Ourselves: Privacy Via Distributed Noise Generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., & Naor, M. (2006) · Lecture Notes in Computer Science · doi:10.1007/11761679_29 · Cited by 1,835
Tools for privacy preserving distributed data mining
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., & Zhu, M. Y. (2002) · ACM SIGKDD Explorations Newsletter · doi:10.1145/772862.772867 · Cited by 890
Cited by
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856
Differential Privacy: A Survey of Results
Dwork, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-79228-4_1 · Cited by 3,542
(2011) · PubMed · Cited by 990
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Hassan, M. U., Rehmani, M. H., & Chen, J. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2944748 · Cited by 564
Resisting structural re-identification in anonymized social networks
Hay, M., Miklau, G., Jensen, D., Towsley, D., & Weis, P. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1453856.1453873 · Cited by 549
Metadata
- Authors
- Nissim, K., Raskhodnikova, S., & Smith, A.
- Venue
- Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
- Publisher
- ACM
- Published
- 2007-06-11
- Pages
- 75-84
- OpenAlex
- W2101771965
- Cited by
- 974
- References
- 3
- FWCI
- 15.18
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Nissim, K., Raskhodnikova, S., & Smith, A. (2007). Smooth sensitivity and sampling in private data analysis. Proceedings of the thirty-ninth annual ACM symposium on Theory of computing. https://doi.org/10.1145/1250790.1250803
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