Optimizing Secure Computation Programs with Private Conditionals

Secure computation platforms are often provided with a programming language that allows a developer to write privacy-preserving applications and hides away the underlying cryptographic details. The control flow of these programs is expensive to hide, hence branching on private values is often disallowed. The application programmers have to specify their programs in terms of allowed constructions, either using ad-hoc methods to avoid such branchings, or the general methodology of executing all branches and obliviously selecting the effects of one at the end. There may be compiler support for the latter.

[1]  Abhi Shelat,et al.  SCORAM: Oblivious RAM for Secure Computation , 2014, IACR Cryptol. ePrint Arch..

[2]  Florian Kerschbaum,et al.  L1 - An Intermediate Language for Mixed-Protocol Secure Computation , 2011, 2011 IEEE 35th Annual Computer Software and Applications Conference.

[3]  Jan Willemson,et al.  Hybrid Model of Fixed and Floating Point Numbers in Secure Multiparty Computations , 2014, ISC.

[4]  Jonathan Katz,et al.  Secure Computation of MIPS Machine Code , 2016, ESORICS.

[5]  Dan Bogdanov,et al.  Sharemind: A Framework for Fast Privacy-Preserving Computations , 2008, ESORICS.

[6]  Yihua Zhang,et al.  PICCO: a general-purpose compiler for private distributed computation , 2013, CCS.

[7]  Tom Schrijvers,et al.  Under Consideration for Publication in Theory and Practice of Logic Programming Swi-prolog , 2022 .

[8]  Vladimir Kolesnikov,et al.  Overlaying Circuit Clauses for Secure Computation , 2016, IACR Cryptol. ePrint Arch..

[9]  Deian Stefan,et al.  Information-Flow Control for Programming on Encrypted Data , 2012, 2012 IEEE 25th Computer Security Foundations Symposium.

[10]  Dan Bogdanov,et al.  Domain-Polymorphic Programming of Privacy-Preserving Applications , 2014, PLAS@ECOOP.

[11]  Benny Pinkas,et al.  Fairplay - Secure Two-Party Computation System (Awarded Best Student Paper!) , 2004 .

[12]  Marina Blanton,et al.  Data-oblivious graph algorithms for secure computation and outsourcing , 2013, ASIA CCS '13.

[13]  Katsumi Takahashi,et al.  Practically Efficient Multi-party Sorting Protocols from Comparison Sort Algorithms , 2012, ICISC.

[14]  Dan Bogdanov,et al.  High-performance secure multi-party computation for data mining applications , 2012, International Journal of Information Security.

[15]  Michael I. Schwartzbach,et al.  A domain-specific programming language for secure multiparty computation , 2007, PLAS '07.

[16]  David K. Smith.  Theory of Linear and Integer Programming , 1987 .

[17]  John C. Mitchell,et al.  Oblivious Program Execution and Path-Sensitive Non-interference , 2013, 2013 IEEE 26th Computer Security Foundations Symposium.

[18]  Ivan Damgård,et al.  Multiparty Computation from Somewhat Homomorphic Encryption , 2012, IACR Cryptol. ePrint Arch..

[19]  Michael Zohner,et al.  ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation , 2015, NDSS.

[20]  Rafail Ostrovsky,et al.  Software protection and simulation on oblivious RAMs , 1996, JACM.

[21]  David Evans,et al.  Obliv-C: A Language for Extensible Data-Oblivious Computation , 2015, IACR Cryptol. ePrint Arch..

[22]  Jonathan Katz,et al.  Secure two-party computation in sublinear (amortized) time , 2012, CCS.

[23]  Xenofontas A. Dimitropoulos,et al.  SEPIA: Privacy-Preserving Aggregation of Multi-Domain Network Events and Statistics , 2010, USENIX Security Symposium.

[24]  Elaine Shi,et al.  Automating Efficient RAM-Model Secure Computation , 2014, 2014 IEEE Symposium on Security and Privacy.

[25]  劉寧漢,et al.  Improving Driver Alertness through Music Selection Using a Mobile EEG to Detect Brainwaves , 2013 .

[26]  Ueli Maurer,et al.  General Secure Multi-party Computation from any Linear Secret-Sharing Scheme , 2000, EUROCRYPT.

[27]  Jan Willemson,et al.  Secure floating point arithmetic and private satellite collision analysis , 2015, International Journal of Information Security.

[28]  Michael Hicks,et al.  Wysteria: A Programming Language for Generic, Mixed-Mode Multiparty Computations , 2014, 2014 IEEE Symposium on Security and Privacy.

[29]  Dan Bogdanov,et al.  Rmind: A Tool for Cryptographically Secure Statistical Analysis , 2016, IEEE Transactions on Dependable and Secure Computing.

[30]  Andrew Chi-Chih Yao,et al.  Protocols for Secure Computations (Extended Abstract) , 1982, FOCS.

[31]  櫻井 幸一,et al.  IEEE Symposium on Security and Privacy 2014 参加報告 , 2012 .

[32]  Stefan Katzenbeisser,et al.  CBMC-GC: An ANSI C Compiler for Secure Two-Party Computations , 2014, CC.

[33]  Kartik Nayak,et al.  ObliVM: A Programming Framework for Secure Computation , 2015, 2015 IEEE Symposium on Security and Privacy.

[34]  Ivan Damgård,et al.  Asynchronous Multiparty Computation: Theory and Implementation , 2008, IACR Cryptol. ePrint Arch..

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