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Volumn 4, Issue , 2014, Pages 2908-2916

Dual query: Practical private query release for high dimensional data

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; INTEGER PROGRAMMING; LEARNING SYSTEMS;

EID: 84919791361     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (27)

References (37)
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    • Privately releasing conjunctions and the statistical query barrier
    • Gupta, Anupam, Hardt, Moritz, Roth, Aaron, and Ullman, Jonathan. Privately releasing conjunctions and the statistical query barrier. SIAM Journal on Computing, 42(4): 1494-1520, 2013.
    • (2013) SIAM Journal on Computing , vol.42 , Issue.4 , pp. 1494-1520
    • Gupta, A.1    Hardt, M.2    Roth, A.3    Ullman, J.4
  • 19
    • 78751489078 scopus 로고    scopus 로고
    • A multiplicative weights mechanism for privacy-preserving data analysis
    • Las Vegas, Nevada
    • Hardt, Moritz and Rothblum, Guy N. A multiplicative weights mechanism for privacy-preserving data analysis. In IEEE Symposium on Foundations of Computer Science (FOCS), Las Ve-gas, Nevada, pp. 61-70, 2010.
    • (2010) IEEE Symposium on Foundations of Computer Science (FOCS) , pp. 61-70
    • Hardt, M.1    Rothblum, G.N.2
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    • A simple and practical algorithm for differentially private data release
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    • Hardt, Moritz, Ligett, Katrina, and McSherry, Frank. A simple and practical algorithm for differentially private data release. In Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, California, pp. 2348-2356, 2012.
    • (2012) Conference on Neural Information Processing Systems (NIPS) , pp. 2348-2356
    • Hardt, M.1    Ligett, K.2    McSherry, F.3
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    • Differential privacy for the analyst via private equilibrium computation
    • Palo Alto, California
    • Hsu, Justin, Roth, Aaron, and Ullman, Jonathan. Differential privacy for the analyst via private equilibrium computation. In ACM SIGACT Symposium on Theory of Computing (STOC), Palo Alto, California, pp. 341-350, 2013.
    • (2013) ACM SIGACT Symposium on Theory of Computing (STOC) , pp. 341-350
    • Hsu, J.1    Roth, A.2    Ullman, J.3
  • 23
    • 0032202014 scopus 로고    scopus 로고
    • Efficient noise-tolerant learning from statistical queries
    • Kearns, Michael J. Efficient noise-tolerant learning from statistical queries. Journal of the ACM, 45(6):983-1006, 1998.
    • (1998) Journal of the ACM , vol.45 , Issue.6 , pp. 983-1006
    • Kearns, M.J.1
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    • Private convex empirical risk minimization and high-dimensional regression
    • Kifer, Daniel, Smith, Adam, and Thakurta, Abhradeep. Private convex empirical risk minimization and high-dimensional regression. Journal of Machine Learning Research, 1:41, 2012.
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    • (Nearly) optimal algorithms for private online learning in full-information and bandit settings
    • Lake Tahoe, California
    • Thakurta, Abhradeep G. and Smith, Adam. (Nearly) optimal algorithms for private online learning in full-information and bandit settings. In Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, California, pp. 2733-2741, 2013.
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    • 2+0(1) counting queries with differential privacy is hard
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    • 2+0(1) counting queries with differential privacy is hard. In ACM SIGACT Symposium on Theory of Computing (STOC), Palo Alto, California, pp. 361-370, 2013.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.