Combining Differential Privacy and Secure Multiparty Computation
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[1] Claude Castelluccia,et al. DREAM: DiffeRentially privatE smArt Metering , 2012, ArXiv.
[2] Elaine Shi,et al. Automating Efficient RAM-Model Secure Computation , 2014, 2014 IEEE Symposium on Security and Privacy.
[3] Kunal Talwar,et al. Mechanism Design via Differential Privacy , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).
[4] Rafail Ostrovsky,et al. Software protection and simulation on oblivious RAMs , 1996, JACM.
[5] Adi Shamir,et al. How to share a secret , 1979, CACM.
[6] Peeter Laud,et al. Automatic Proofs of Privacy of Secure Multi-party Computation Protocols against Active Adversaries , 2015, 2015 IEEE 28th Computer Security Foundations Symposium.
[7] Andrew Chi-Chih Yao,et al. Protocols for Secure Computations (Extended Abstract) , 1982, FOCS.
[8] Daniel A. Spielman,et al. Spectral Graph Theory and its Applications , 2007, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07).
[9] Claude Castelluccia,et al. I Have a DREAM! (DiffeRentially privatE smArt Metering) , 2011, Information Hiding.
[10] Moni Naor,et al. Our Data, Ourselves: Privacy Via Distributed Noise Generation , 2006, EUROCRYPT.
[11] Riivo Talviste,et al. From Oblivious AES to Efficient and Secure Database Join in the Multiparty Setting , 2013, ACNS.
[12] Frank McSherry,et al. Privacy integrated queries: an extensible platform for privacy-preserving data analysis , 2009, SIGMOD Conference.
[13] Liina Kamm,et al. Privacy-preserving statistical analysis using secure multi-party computation , 2015 .
[14] Dan Bogdanov,et al. Sharemind: A Framework for Fast Privacy-Preserving Computations , 2008, ESORICS.
[15] Andreas Haeberlen,et al. Differential Privacy: An Economic Method for Choosing Epsilon , 2014, 2014 IEEE 27th Computer Security Foundations Symposium.
[16] Aniket Kate,et al. Differentially private data aggregation with optimal utility , 2014, ACSAC '14.
[17] Silvio Micali,et al. How to play ANY mental game , 1987, STOC.
[18] Marcel Keller,et al. An architecture for practical actively secure MPC with dishonest majority , 2013, IACR Cryptol. ePrint Arch..
[19] Elaine Shi,et al. GUPT: privacy preserving data analysis made easy , 2012, SIGMOD Conference.
[20] Peeter Laud,et al. Parallel Oblivious Array Access for Secure Multiparty Computation and Privacy-Preserving Minimum Spanning Trees , 2015, Proc. Priv. Enhancing Technol..
[21] Dan Bogdanov,et al. High-performance secure multi-party computation for data mining applications , 2012, International Journal of Information Security.
[22] Ivan Damgård,et al. Perfectly Secure Oblivious RAM Without Random Oracles , 2011, IACR Cryptol. ePrint Arch..
[23] Donald Beaver,et al. Efficient Multiparty Protocols Using Circuit Randomization , 1991, CRYPTO.
[24] Tal Rabin,et al. Simplified VSS and fast-track multiparty computations with applications to threshold cryptography , 1998, PODC '98.
[25] Catuscia Palamidessi,et al. Broadening the Scope of Differential Privacy Using Metrics , 2013, Privacy Enhancing Technologies.
[26] Vaidy S. Sunderam,et al. Secure multiparty aggregation with differential privacy: a comparative study , 2013, EDBT '13.
[27] Ivan Damgård,et al. Multiparty Computation from Somewhat Homomorphic Encryption , 2012, IACR Cryptol. ePrint Arch..
[28] Jan Willemson,et al. Secure floating point arithmetic and private satellite collision analysis , 2015, International Journal of Information Security.
[29] R. Cramer,et al. Multiparty Computation from Threshold Homomorphic Encryption , 2000 .
[30] Adam D. Smith,et al. Privacy-preserving statistical estimation with optimal convergence rates , 2011, STOC '11.
[31] David Sands,et al. Differential Privacy , 2015, POPL.
[32] Steven Myers,et al. GPU and CPU parallelization of honest-but-curious secure two-party computation , 2013, ACSAC.
[33] Octavian Catrina,et al. Improved Primitives for Secure Multiparty Integer Computation , 2010, SCN.
[34] Andrew Chi-Chih Yao,et al. Protocols for secure computations , 1982, FOCS 1982.
[35] Sofya Raskhodnikova,et al. Smooth sensitivity and sampling in private data analysis , 2007, STOC '07.
[36] Patrick Traynor,et al. Whitewash: outsourcing garbled circuit generation for mobile devices , 2014, ACSAC.