Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be trusted and provides only weakest-link security (e.g., the entity that manages private/secret cryptographic keys), or leverage on decentralized but impractical approaches (e.g., secure multi-party computation). When the data to be shared are of a sensitive nature and the number of data providers is high, these solutions are not appropriate. Therefore, we present UnLynx, a new decentralized system for efficient privacy-preserving data sharing. We consider m servers that constitute a collective authority whose goal is to verifiably compute on data sent from n data providers. UnLynx guarantees the confidentiality, unlinkability between data providers and their data, privacy of the end result and the correctness of computations by the servers. Furthermore, to support differentially private queries, UnLynx can collectively add noise under encryption. All of this is achieved through a combination of a set of new distributed and secure protocols that are based on homomorphic cryptography, verifiable shuffling and zero-knowledge proofs. UnLynx is highly parallelizable and modular by design as it enables multiple security/privacy vs. runtime tradeoffs. Our evaluation shows that UnLynx can execute a secure survey on 400,000 personal data records containing 5 encrypted attributes, distributed over 20 independent databases, for a total of 2,000,000 ciphertexts, in 24 minutes.

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  • [1] Bluekrypt cryptographic key length recommendation. https://www.keylength.com/fr/4/#Biblio4.

  • [2] DeDiS Research Lab at EPFL advanced crypto library for the Go language. https://github.com/DeDiS/crypto.

  • [3] Dyadic security. https://www.dyadicsec.com/.

  • [4] General Data Protection Regulation. http://ec.europa.eu/justice/data-protection/international-transfers/index_en.htm.

  • [5] The Go Programming Language. https://golang.org.

  • [6] Mininet An Instant Virtual Network. http://mininet.org.

  • [7] Unlynx experimental implementation. https://github.com/lca1/unlynx.

  • [8] What is the Future of Data Sharing? http://www8.gsb.columbia.edu/globalbrands/sites/globalbrands/files/images/The_Future_of_Data_Sharing_Columbia-Aimia_October_2015.pdf.

  • [9] B. Anandan and C. Clifton. Laplace noise generation for two-party computational differential privacy. In 13th Annual Conference on Privacy Security and Trust (PST) pages 54–61 2015.

  • [10] Dixie B. Baker Jane Kaye and Sharon F. Terry. Privacy Fairness and Respect for Individuals. eGEMS (Generating Evidence & Methods to Improve Patient Outcomes) 4(2) 2016.

  • [11] M. Bellare V. T. Hoang S. Keelveedhi and P. Rogaway. Efficient Garbling from a Fixed-Key Blockcipher. In 2013 IEEE Symposium on Security and Privacy (SP) pages 478–492 May 2013.

  • [12] D. J. Bernstein N. Duif T. Lange P. Schwabe and B.-Y. Yang. High-speed high-security signatures. Journal of Cryptographic Engineering 2 pages 77–89 2012.

  • [13] Dan Bogdanov Liina Kamm Baldur Kubo Reimo Rebane Ville Sokk and Riivo Talviste. Students and taxes: a privacy-preserving study using secure computation. In Proceedings on Privacy Enhancing Technologies 2016 2016.

  • [14] Dan Bogdanov Sven Laur and Jan Willemson. Sharemind: A framework for fast privacy-preserving computations. In European Symposium on Research in Computer Security pages 192–206. Springer 2008.

  • [15] Jan Camenisch Rafik Chaabouni and Abhi Shelat. Efficient protocols for set membership and range proofs. In ASIACRYPT 2008 pages 234–252 2008.

  • [16] Jan Camenisch and Markus Stadler. Proof systems for general statements about discrete logarithms. Technical Report (260) 1997.

  • [17] R. Chen A. Reznichenko P. Francis and J. Gehrke. Statistical queries over distributed private user data. In NSDI. Vol. 12 2012.

  • [18] Benny Chor Shafi Goldwasser Silvio Micali and Baruch Awerbuch. Verifiable secret sharing and achieving simultaneity in the presence of faults. In 26th Annual Symposium on Foundations of Computer Science pages 383–395. IEEE 1985.

  • [19] Tulio de Souza Joss Wright Piers O’Hanlon and Ian Brown. Set difference attacks in wireless sensor networks. International Conference on Security and Privacy in Communication Systems 2012.

  • [20] Xin Dong Jiadi Yu Yuan Luo Yingying Chen Guangtao Xue and Minglu Li. Achieving an effective scalable and privacy-preserving data sharing service in cloud computing. Computers & security 42:151–164 2014.

  • [21] Yitao Duan John Canny and Justin Zhan. Efficient privacy-preserving association rule mining: P4P style. In Symposium on Computational Intelligence and Data Mining pages 654–660. IEEE 2007.

  • [22] C. Dwork K. Kenthapadi F. McSherry I. Mironov and M. Naor. Our data ourselves: Privacy via distributed noise generation. In Annual International Conference on the Theory and Applications of Cryptographic Techniques pages 486–503. Springer Berlin Heidelberg 2006.

  • [23] Cynthia Dwork. Differential privacy. Venice Italy July 2006. Springer Verlag.

  • [24] Cynthia Dwork. A firm foundation for private data analysis. In Communications of the ACM 54(1) pages 86–95 2011.

  • [25] Cynthia Dwork Frank McSherry Kobbi Nissim and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography Conference pages 265–284. Springer 2006.

  • [26] Benjamin Fabian Tatiana Ermakova and Philipp Junghanns. Collaborative and secure sharing of healthcare data in multi-clouds. Information Systems 48:132–150 March 2015.

  • [27] Amos Fiat and Adi Shamir. How to prove yourself: Practical solutions to identification and signature problems. In Conference on the Theory and Application of Cryptographic Techniques pages 186–194. Springer 1986.

  • [28] Chang Liu Xiao Shaun Wang K. Nayak Yan Huang and E. Shi. ObliVM: A Programming Framework for Secure Computation. In 2015 IEEE Symposium on Security and Privacy (SP) pages 359–376 May 2015.

  • [29] F. Liu Y. Yarom Q. Ge G. Heiser and R. B. Lee. Last-Level Cache Side-Channel Attacks are Practical. In 2015 IEEE Symposium on Security and Privacy pages 605–622 May 2015.

  • [30] Xuefeng Liu Yuqing Zhang Boyang Wang and Jingbo Yan. Mona: secure multi-owner data sharing for dynamic groups in the cloud. IEEE Transactions on Parallel and Distributed Systems 24(6):1182–1191 2013.

  • [31] N. Mohammed D. Alhadidi BCM. Fung and M. Debbabi. Secure two-party differentially private data release for vertically partitioned data. In IEEE Trans Dependable Secur Comput 11 pages 59–71 2014.

  • [32] A. Narayan and A. Haeberlen. Djoin: Differentially private join queries over distributed databases. In Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation OSDI’12 pages 149–162 2012.

  • [33] K. Nayak X. S. Wang S. Ioannidis U. Weinsberg N. Taft and E. Shi. GraphSC: Parallel Secure Computation Made Easy. In 2015 IEEE Symposium on Security and Privacy (SP) pages 377–394 May 2015.

  • [34] C Andrew Neff. Verifiable mixing (shuffling) of ElGamal pairs (2004).

  • [35] C Andrew Neff. A verifiable secret shuffle and its application to e-voting. In Proceedings ACM-CCS 2001 pages 116–125 2001.

  • [36] Wee Siong Ng Beng Chin Ooi Kian-Lee Tan and Aoying Zhou. PeerDB: A P2P-based system for distributed data sharing. In Data Engineering 2003. Proceedings. 19th International Conference on pages 633–644. IEEE 2003.

  • [37] Olga Ohrimenko Manuel Costa Cédric Fournet Christos Gkantsidis Markulf Kohlweiss and Divya Sharma. Observing and Preventing Leakage in MapReduce. In Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security pages 1570–1581 2015.

  • [38] Olga Ohrimenko Felix Schuster Cédric Fournet Aastha Mehta Sebastian Nowozin Kapil Vaswani and Manuel Costa. Oblivious multi-party machine learning on trusted processors. In 25th USENIX Security Symposium (USENIX Security 16) 2016.

  • [39] Raluca Ada Popa Catherine Redfield Nickolai Zeldovich and Hari Balakrishnan. CryptDB: protecting confidentiality with encrypted query processing. In Proceedings of the Twenty-Third ACM Symposium on Operating Systems Principles pages 85–100. ACM 2011.

  • [40] A. Rastogi M. A. Hammer and M. Hicks. Wysteria: A Programming Language for Generic Mixed-Mode Multiparty Computations. In 2014 IEEE Symposium on Security and Privacy pages 655–670 May 2014.

  • [41] F. Schuster M. Costa C. Fournet C. Gkantsidis M. Peinado G. Mainar-Ruiz and M. Russinovich. VC3: Trustworthy Data Analytics in the Cloud Using SGX. In 2015 IEEE Symposium on Security and Privacy pages 38–54 May 2015.

  • [42] E. M. Songhori S. U. Hussain A. R. Sadeghi T. Schneider and F. Koushanfar. TinyGarble: Highly Compressed and Scalable Sequential Garbled Circuits. In 2015 IEEE Symposium on Security and Privacy pages 411–428 May 2015.

  • [43] L Sweeney. k-anonymity: A Model for Protecting Privacy. International Journal on Uncertainty Fuzziness and Knowledge-based Systems 10(5):557–570 2002.

  • [44] Ewa Syta Iulia Tamas Dylan Visher David Isaac Wolinsky Philipp Jovanovic Linus Gasser Nicolas Gailly Ismail Khoffi and Bryan Ford. Keeping Authorities” Honest or Bust” with Decentralized Witness Cosigning. arXiv preprint arXiv:1503.08768 2015.

  • [45] U.S. Department of Health and Human Services. Breach portal: Notice to the secretary of hhs breach of unsecured protected health information. https://ocrportal.hhs.gov/ocr/breach/breach_report.jsf. Last Accessed: September 27 2017.

  • [46] David I Wolinsky Henry Corrigan-Gibbs Bryan Ford and Aaron Johnson. Scalable anonymous group communication in the anytrust model. In 5th European Workshop on System Security 2012.

  • [47] Yuanzhong Xu Weidong Cui and Marcus Peinado. Controlled-Channel Attacks: Deterministic Side Channels for Untrusted Operating Systems. In Proceedings of the 2015 IEEE Symposium on Security and Privacy pages 640–656 2015.

  • [48] Min Yang and Yuanyuan Yang. An efficient hybrid peer-to-peer system for distributed data sharing. IEEE Transactions on computers 59(9):1158–1171 2010.

  • [49] Mahdi Zamani Mahnush Movahedi and Jared Saia. Millions of millionaires: Multiparty computation in large networks. IACR Cryptology ePrint Archive 2014:149 2014.

  • [50] Ning Zhang Ming Li and Wenjing Lou. Distributed data mining with differential privacy. In 2011 IEEE International Conference on Communications (ICC) pages 1–5. IEEE 2011.

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