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Volumn 8096 LNCS, Issue , 2013, Pages 363-378

Private learning and sanitization: Pure vs. approximate differential privacy

Author keywords

Differential Privacy; Private Learning; Sanitization

Indexed keywords

DIFFERENTIAL PRIVACIES; PRIVATE LEARNING; SAMPLE COMPLEXITY; SANITIZATION;

EID: 84885226635     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40328-6_26     Document Type: Conference Paper
Times cited : (133)

References (22)
  • 1
    • 77949617188 scopus 로고    scopus 로고
    • Bounds on the sample complexity for private learning and private data release
    • Micciancio, D. (ed.) TCC 2010. Springer, Heidelberg
    • Beimel, A., Kasiviswanathan, S.P., Nissim, K.: Bounds on the sample complexity for private learning and private data release. In: Micciancio, D. (ed.) TCC 2010. LNCS, vol. 5978, pp. 437-454. Springer, Heidelberg (2010)
    • (2010) LNCS , vol.5978 , pp. 437-454
    • Beimel, A.1    Kasiviswanathan, S.P.2    Nissim, K.3
  • 2
    • 84873351478 scopus 로고    scopus 로고
    • Characterizing the sample complexity of private learners
    • Beimel, A., Nissim, K., Stemmer, U.: Characterizing the sample complexity of private learners. In: ITCS, pp. 97-110 (2013)
    • (2013) ITCS , pp. 97-110
    • Beimel, A.1    Nissim, K.2    Stemmer, U.3
  • 3
    • 33244468835 scopus 로고    scopus 로고
    • Practical privacy: The SuLQ framework
    • ACM
    • Blum, A., Dwork, C., McSherry, F., Nissim, K.: Practical privacy: The SuLQ framework. In: PODS, pp. 128-138. ACM (2005)
    • (2005) PODS , pp. 128-138
    • Blum, A.1    Dwork, C.2    McSherry, F.3    Nissim, K.4
  • 4
    • 84877909042 scopus 로고    scopus 로고
    • A learning theory approach to noninteractive database privacy
    • Blum, A., Ligett, K., Roth, A.: A learning theory approach to noninteractive database privacy. J. ACM 60(2), 12:1-12:25 (2013)
    • (2013) J. ACM , vol.60 , Issue.2
    • Blum, A.1    Ligett, K.2    Roth, A.3
  • 5
    • 84885588035 scopus 로고    scopus 로고
    • Sample complexity bounds for differentially private learning
    • Chaudhuri, K., Hsu, D.: Sample complexity bounds for differentially private learning. In: COLT, vol. 19, pp. 155-186 (2011)
    • (2011) COLT , vol.19 , pp. 155-186
    • Chaudhuri, K.1    Hsu, D.2
  • 6
    • 84858307870 scopus 로고    scopus 로고
    • Lower bounds in differential privacy
    • Cramer, R. (ed.) TCC 2012. Springer, Heidelberg
    • De, A.: Lower bounds in differential privacy. In: Cramer, R. (ed.) TCC 2012. LNCS, vol. 7194, pp. 321-338. Springer, Heidelberg (2012)
    • (2012) LNCS , vol.7194 , pp. 321-338
    • De, A.1
  • 8
    • 70350682013 scopus 로고    scopus 로고
    • Differential privacy and robust statistics
    • ACM, New York
    • Dwork, C., Lei, J.: Differential privacy and robust statistics. In: STOC 2009, pp. 371-380. ACM, New York (2009)
    • (2009) STOC 2009 , pp. 371-380
    • Dwork, C.1    Lei, J.2
  • 10
    • 70350689921 scopus 로고    scopus 로고
    • On the complexity of differentially private data release
    • ACM
    • Dwork, C., Naor, M., Reingold, O., Rothblum, G., Vadhan, S.: On the complexity of differentially private data release. In: STOC, pp. 381-390. ACM (2009)
    • (2009) STOC , pp. 381-390
    • Dwork, C.1    Naor, M.2    Reingold, O.3    Rothblum, G.4    Vadhan, S.5
  • 11
    • 79959740503 scopus 로고    scopus 로고
    • Privately releasing conjunctions and the statistical query barrier
    • ACM, New York
    • Gupta, A., Hardt, M., Roth, A., Ullman, J.: Privately releasing conjunctions and the statistical query barrier. In: STOC, pp. 803-812. ACM, New York (2011)
    • (2011) STOC , pp. 803-812
    • Gupta, A.1    Hardt, M.2    Roth, A.3    Ullman, J.4
  • 12
    • 77954711905 scopus 로고    scopus 로고
    • On the geometry of differential privacy
    • Hardt, M., Talwar, K.: On the geometry of differential privacy. In: STOC,7 pp. 705-714 (2010)
    • (2010) STOC , vol.7 , pp. 705-714
    • Hardt, M.1    Talwar, K.2
  • 15
    • 0032202014 scopus 로고    scopus 로고
    • Efficient noise-tolerant learning from statistical queries
    • Kearns, M.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
  • 17
    • 46749128577 scopus 로고    scopus 로고
    • Mechanism design via differential privacy
    • IEEE
    • McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: FOCS, pp. 94-103. IEEE (2007)
    • (2007) FOCS , pp. 94-103
    • McSherry, F.1    Talwar, K.2
  • 18
    • 78149311849 scopus 로고    scopus 로고
    • Differential privacy and the fat-shattering dimension of linear queries
    • Serna, M., Shaltiel, R., Jansen, K., Rolim, J. (eds.) APPROX and RANDOM 2010. Springer, Heidelberg
    • Roth, A.: Differential privacy and the fat-shattering dimension of linear queries. In: Serna, M., Shaltiel, R., Jansen, K., Rolim, J. (eds.) APPROX and RANDOM 2010. LNCS, vol. 6302, pp. 683-695. Springer, Heidelberg (2010)
    • (2010) LNCS , vol.6302 , pp. 683-695
    • Roth, A.1
  • 20
    • 84885236790 scopus 로고    scopus 로고
    • 2+o(1) counting queries with differential privacy is hard
    • abs/1207.6945
    • 2+o(1) counting queries with differential privacy is hard. CoRR, abs/1207.6945 (2012)
    • (2012) CoRR
    • Ullman, J.1
  • 21
    • 79953172891 scopus 로고    scopus 로고
    • PCPs and the hardness of generating private synthetic data
    • Ishai, Y. (ed.) TCC 2011. Springer, Heidelberg
    • Ullman, J., Vadhan, S.: PCPs and the hardness of generating private synthetic data. In: Ishai, Y. (ed.) TCC 2011. LNCS, vol. 6597, pp. 400-416. Springer, Heidelberg (2011)
    • (2011) LNCS , vol.6597 , pp. 400-416
    • Ullman, J.1    Vadhan, S.2
  • 22


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