The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
A Statistical Framework for Differential Privacy
Open accessAbstract
One goal of statistical privacy research is to construct a data release mechanism that protects individual privacy while preserving information content. An example is a random mechanism that takes an input database X and outputs a random database Z according to a distribution Qn(⋅|X). Differential privacy is a particular privacy requirement developed by computer scientists in which Qn(⋅|X) is required to be insensitive to changes in one data point in X. This makes it difficult to infer from Z whether a given individual is in the original database X. We consider differential privacy from a statistical perspective. We consider several data-release mechanisms that satisfy the differential privacy requirement. We show that it is useful to compare these schemes by computing the rate of convergence of distributions and densities constructed from the released data. We study a general privacy method, called the exponential mechanism, introduced by McSherry and Talwar (2007). We show that the accuracy of this method is intimately linked to the rate at which the probability that the empirical distribution concentrates in a small ball around the true distribution.
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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
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
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
Warner, S. L. (1965) · Journal of the American Statistical Association · doi:10.2307/2283137 · Cited by 3,263
Cited by
(2011) · PubMed · Cited by 990
Kasiviswanathan, S. P., Lee, H. K., Nissim, K., Raskhodnikova, S., & Smith, A. (2011) · SIAM Journal on Computing · doi:10.1137/090756090 · Cited by 898
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
The composition theorem for differential privacy
Kairouz, P., Oh, S., & Viswanath, P. (2017) · IEEE Transactions on Information Theory · doi:10.1109/tit.2017.2685505 · Cited by 408
Dong, J., Roth, A., & Su, W. J. (2019) · arXiv · doi:10.48550/arxiv.1905.02383 · Cited by 389
Big privacy: challenges and opportunities of privacy study in the age of big data
Yu, S. (2016) · IEEE Access · doi:10.1109/access.2016.2577036 · Cited by 307
Coffman, K. B., Coffman, L. C., & Ericson, K. M. M. (2017) · Management Science · doi:10.1287/mnsc.2016.2503 · Cited by 287
Exposed! A Survey of Attacks on Private Data
Dwork, C., Smith, A., Steinke, T., & Ullman, J. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-060116-054123 · Cited by 259
An economic analysis of privacy protection and statistical accuracy as social choices
Abowd, J. M., & Schmutte, I. M. (2019) · American Economic Review · doi:10.1257/aer.20170627 · Cited by 162
Learning Anonymized Representations with Adversarial Neural Networks
Feutry, C., Piantanida, P., Bengio, Y., & Duhamel, P. (2018) · arXiv · doi:10.48550/arxiv.1802.09386 · Cited by 52
Improving Data Utility Through Game Theory in Personalized Differential Privacy
Cui, L., Qu, Y., Nosouhi, M. R., Yu, S., Niu, J.-W., & Xie, G. (2019) · Journal of Computer Science and Technology · doi:10.1007/s11390-019-1910-3 · Cited by 31
On the Power of Multiple Anonymous Messages
Ghazi, B., Golowich, N., Kumar, R., Pagh, R., & Velingker, A. (2019) · arXiv · doi:10.48550/arxiv.1908.11358 · Cited by 26
Integrating Technical and Legal Concepts of Privacy
Sokolovska, A., & Kocarev, L. (2018) · IEEE Access · doi:10.1109/access.2018.2836184 · Cited by 24
Metadata
- Authors
- Wasserman, L., & Zhou, S.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2010-03-01
- Volume
- 105
- Issue
- 489
- Pages
- 375-389
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2074006684
- Cited by
- 397
- References
- 4
- FWCI
- 13.60
- Type
- journal article
- Language
- en
- Open access
- green
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