Private Learning and Sanitization: Pure vs. Approximate Differential Privacy

We compare the sample complexity of private learning and sanitization tasks under pure e-differential privacy [Dwork, McSherry, Nissim, and Smith TCC 2006] and approximate (e,δ)-differential privacy [Dwork, Kenthapadi, McSherry, Mironov, and Naor EUROCRYPT 2006]. We show that the sample complexity of these tasks under approximate differential privacy can be significantly lower than that under pure differential privacy.

[1]  Jonathan Ullman,et al.  Answering n{2+o(1)} counting queries with differential privacy is hard , 2012, STOC '13.

[2]  Adam D. Smith,et al.  Differentially Private Feature Selection via Stability Arguments, and the Robustness of the Lasso , 2013, COLT.

[3]  David Haussler,et al.  Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.

[4]  Guy N. Rothblum,et al.  Boosting and Differential Privacy , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.

[5]  Kamalika Chaudhuri,et al.  Sample Complexity Bounds for Differentially Private Learning , 2011, COLT.

[6]  Guy N. Rothblum,et al.  A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.

[7]  Kunal Talwar,et al.  On the geometry of differential privacy , 2009, STOC '10.

[8]  Anindya De,et al.  Lower Bounds in Differential Privacy , 2011, TCC.

[9]  Vitaly Feldman,et al.  Sample Complexity Bounds on Differentially Private Learning via Communication Complexity , 2014, SIAM J. Comput..

[10]  Aaron Roth Differential Privacy and the Fat-Shattering Dimension of Linear Queries , 2010, APPROX-RANDOM.

[11]  Michael Kearns,et al.  Efficient noise-tolerant learning from statistical queries , 1993, STOC.

[12]  Amos Beimel,et al.  Bounds on the sample complexity for private learning and private data release , 2010, Machine Learning.

[13]  Sofya Raskhodnikova,et al.  What Can We Learn Privately? , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.

[14]  Aaron Roth,et al.  A learning theory approach to non-interactive database privacy , 2008, STOC.

[15]  Kunal Talwar,et al.  Mechanism Design via Differential Privacy , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[16]  John Shawe-Taylor,et al.  A Result of Vapnik with Applications , 1993, Discret. Appl. Math..

[17]  Cynthia Dwork,et al.  Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.

[18]  Leslie G. Valiant,et al.  A general lower bound on the number of examples needed for learning , 1988, COLT '88.

[19]  B. Barak,et al.  A study of privacy and fairness in sensitive data analysis , 2011 .

[20]  Moni Naor,et al.  On the complexity of differentially private data release: efficient algorithms and hardness results , 2009, STOC '09.

[21]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[22]  Suresh Jagannathan,et al.  CompCertTSO: A Verified Compiler for Relaxed-Memory Concurrency , 2013, JACM.

[23]  Amos Beimel,et al.  Characterizing the sample complexity of private learners , 2013, ITCS '13.

[24]  Jonathan Ullman,et al.  PCPs and the Hardness of Generating Private Synthetic Data , 2011, TCC.

[25]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[26]  Daniel A. Spielman,et al.  Spectral Graph Theory and its Applications , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).

[27]  Moni Naor,et al.  Our Data, Ourselves: Privacy Via Distributed Noise Generation , 2006, EUROCRYPT.

[28]  Peter L. Bartlett,et al.  Neural Network Learning - Theoretical Foundations , 1999 .

[29]  Aaron Roth,et al.  Privately releasing conjunctions and the statistical query barrier , 2010, STOC '11.

[30]  Cynthia Dwork,et al.  Practical privacy: the SuLQ framework , 2005, PODS.

[31]  Cynthia Dwork,et al.  Differential privacy and robust statistics , 2009, STOC '09.