A Privacy-Preserving Outsourced Functional Computation Framework Across Large-Scale Multiple Encrypted Domains

In this paper, we propose a framework for privacy-preserving outsourced functional computation across large-scale multiple encrypted domains, which we refer to as POFD. With POFD, a user can obtain the output of a function computed over encrypted data from multiple domains while protecting the privacy of the function itself, its input and its output. Specifically, we introduce two notions of POFD, the basic POFD and its enhanced version, in order to tradeoff the levels of privacy protection and performance. We present three protocols, named Multi-domain Secure Multiplication protocol (MSM), Secure Exponent Calculation protocol with private Base (SECB), and Secure Exponent Calculation protocol (SEC), as the core sub-protocols for POFD to securely compute the outsourced function. Detailed security analysis shows that the proposed POFD achieves the goal of calculating a user-defined function across different encrypted domains without privacy leakage to unauthorized parties. Our performance evaluations using simulations demonstrate the utility and the efficiency of POFD.

[1]  Vinod Vaikuntanathan,et al.  On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption , 2012, STOC '12.

[2]  Young-Chan Lee,et al.  Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters , 2005, Expert Syst. Appl..

[3]  Wei Jiang,et al.  k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[4]  Eric A. Fischer,et al.  Overview and Issues for Implementation of the Federal Cloud Computing Initiative: Implications for Federal Information Technology Reform Management , 2013 .

[5]  Robert H. Deng,et al.  An Efficient Privacy-Preserving Outsourced Computation over Public Data , 2017, IEEE Transactions on Services Computing.

[6]  Dongxi Liu,et al.  Privacy-Preserving and Outsourced Multi-user K-Means Clustering , 2014, 2015 IEEE Conference on Collaboration and Internet Computing (CIC).

[7]  Bharath K. Samanthula,et al.  D ec 2 01 4 Privacy-Preserving and Outsourced Multi-User k-Means Clustering , 2015 .

[8]  Zhirong Sun,et al.  Support vector machine approach for protein subcellular localization prediction , 2001, Bioinform..

[9]  Oded Goldreich,et al.  Foundations of Cryptography: Volume 2, Basic Applications , 2004 .

[10]  Iftekhar Ahmad,et al.  SVM based models for predicting foreign currency exchange rates , 2003, Third IEEE International Conference on Data Mining.

[11]  Robert H. Deng,et al.  Efficient and Privacy-Preserving Outsourced Calculation of Rational Numbers , 2018, IEEE Transactions on Dependable and Secure Computing.

[12]  Liam Morris,et al.  Analysis of Partially and Fully Homomorphic Encryption , 2013 .

[13]  Wen-Guey Tzeng,et al.  An Efficient Solution to the Millionaires' Problem Based on Homomorphic Encryption , 2005, ACNS.

[14]  A. Salomaa,et al.  Chinese remainder theorem: applications in computing, coding, cryptography , 1996 .

[15]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[16]  Jianfeng Ma,et al.  Efficient and privacy-preserving skyline computation framework across domains , 2016, Future Gener. Comput. Syst..

[17]  Frederik Vercauteren,et al.  Fully Homomorphic Encryption with Relatively Small Key and Ciphertext Sizes , 2010, Public Key Cryptography.

[18]  Jean-Sébastien Coron,et al.  Scale-Invariant Fully Homomorphic Encryption over the Integers , 2014, Public Key Cryptography.

[19]  Oded Goldreich,et al.  The Foundations of Cryptography - Volume 2: Basic Applications , 2001 .

[20]  Lance J. Hoffman,et al.  Trust beyond security: an expanded trust model , 2006, CACM.

[21]  Ronald Cramer,et al.  Universal Hash Proofs and a Paradigm for Adaptive Chosen Ciphertext Secure Public-Key Encryption , 2001, EUROCRYPT.

[22]  Adi Shamir,et al.  How to share a secret , 1979, CACM.

[23]  Silvio Micali,et al.  How to play ANY mental game , 1987, STOC.

[24]  Craig Gentry,et al.  Two-Round Secure MPC from Indistinguishability Obfuscation , 2014, TCC.

[25]  Xiaodong Lin,et al.  SPOC: A Secure and Privacy-Preserving Opportunistic Computing Framework for Mobile-Healthcare Emergency , 2013, IEEE Transactions on Parallel and Distributed Systems.

[26]  Jonathan Katz,et al.  Faster Secure Two-Party Computation Using Garbled Circuits , 2011, USENIX Security Symposium.

[27]  Taher ElGamal,et al.  A public key cyryptosystem and signature scheme based on discrete logarithms , 1985 .

[28]  Rafail Ostrovsky,et al.  5PM: Secure pattern matching , 2012, J. Comput. Secur..

[29]  Stefan Katzenbeisser,et al.  Efficiently Outsourcing Multiparty Computation Under Multiple Keys , 2013, IEEE Transactions on Information Forensics and Security.

[30]  Joseph K. Liu,et al.  Toward efficient and privacy-preserving computing in big data era , 2014, IEEE Network.

[31]  Amit Sahai,et al.  Secure Multi-Party Computation , 2013 .

[32]  Leslie Lamport,et al.  Password authentication with insecure communication , 1981, CACM.

[33]  Pascal Paillier,et al.  Public-Key Cryptosystems Based on Composite Degree Residuosity Classes , 1999, EUROCRYPT.

[34]  Vivek Kundra,et al.  Federal Cloud Computing Strategy , 2011 .

[35]  C. Ding.  Chinese remainder theorem , 1996 .

[36]  Emmanuel Bresson,et al.  A Simple Public-Key Cryptosystem with a Double Trapdoor Decryption Mechanism and Its Applications , 2003, ASIACRYPT.

[37]  Jianfeng Ma,et al.  Privacy-Preserving Patient-Centric Clinical Decision Support System on Naïve Bayesian Classification , 2016, IEEE Journal of Biomedical and Health Informatics.

[38]  Wei Jiang,et al.  Secure k-nearest neighbor query over encrypted data in outsourced environments , 2013, 2014 IEEE 30th International Conference on Data Engineering.

[39]  Moshe Zviran,et al.  Does color in email make a difference? , 2006, Commun. ACM.

[40]  Josh Benaloh,et al.  Dense Probabilistic Encryption , 1999 .

[41]  D. E. Knuth.  Seminumerical algorithm (arithmetic) , 1969 .

[42]  Brent Waters,et al.  Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based , 2013, CRYPTO.

[43]  Benny Pinkas,et al.  Fairplay - Secure Two-Party Computation System , 2004, USENIX Security Symposium.

[44]  Adi Shamir,et al.  A method for obtaining digital signatures and public-key cryptosystems , 1978, CACM.

[45]  Emmanuel Aroms.  NIST Special Publication 800-55 Rev1 Security Metrics Guide for Information Technology Systems , 2012 .

[46]  Yuval Ishai,et al.  Constant-Round Multiparty Computation Using a Black-Box Pseudorandom Generator , 2005, CRYPTO.

[47]  Andrew Chi-Chih Yao,et al.  Protocols for secure computations , 1982, FOCS 1982.

[48]  Mariana Raykova,et al.  Outsourcing Multi-Party Computation , 2011, IACR Cryptol. ePrint Arch..

[49]  Craig Gentry,et al.  Fully homomorphic encryption using ideal lattices , 2009, STOC '09.

[50]  Marios D. Dikaiakos,et al.  Cloud Computing: Distributed Internet Computing for IT and Scientific Research , 2009, IEEE Internet Computing.

[51]  Lizhe Wang,et al.  Scientific Cloud Computing: Early Definition and Experience , 2008, 2008 10th IEEE International Conference on High Performance Computing and Communications.