Founding crytpography on oblivious transfer
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Efficient oblivious transfer protocols
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Cryptographic techniques for privacy-preserving data mining
ACM SIGKDD Explorations Newsletter
Non-Interactive CryptoComputing For NC1
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Secure function evaluation with ordered binary decision diagrams
Proceedings of the 13th ACM conference on Computer and communications security
Improved error reporting for software that uses black-box components
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Privacy-preserving remote diagnostics
Proceedings of the 14th ACM conference on Computer and communications security
Oblivious neural network computing via homomorphic encryption
EURASIP Journal on Information Security
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Privacy-Preserving Classifier Learning
Financial Cryptography and Data Security
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Evaluating branching programs on encrypted data
TCC'07 Proceedings of the 4th conference on Theory of cryptography
Generating estimates of classification confidence for a case-based spam filter
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Real-time classification of ECGs on a PDA
IEEE Transactions on Information Technology in Biomedicine
Improved Garbled Circuit Building Blocks and Applications to Auctions and Computing Minima
CANS '09 Proceedings of the 8th International Conference on Cryptology and Network Security
TASTY: tool for automating secure two-party computations
Proceedings of the 17th ACM conference on Computer and communications security
Efficient privacy-preserving face recognition
ICISC'09 Proceedings of the 12th international conference on Information security and cryptology
Processing encrypted floating point signals
Proceedings of the thirteenth ACM multimedia workshop on Multimedia and security
WISS'10 Proceedings of the 2010 international conference on Web information systems engineering
Constant-Round private function evaluation with linear complexity
ASIACRYPT'11 Proceedings of the 17th international conference on The Theory and Application of Cryptology and Information Security
Secure two-party computations in ANSI C
Proceedings of the 2012 ACM conference on Computer and communications security
Foundations of garbled circuits
Proceedings of the 2012 ACM conference on Computer and communications security
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Diagnostic and classification algorithms play an important role in data analysis, with applications in areas such as health care, fault diagnostics, or benchmarking. Branching programs (BP) is a popular representation model for describing the underlying classification/diagnostics algorithms. Typical application scenarios involve a client who provides data and a service provider (server) whose diagnostic program is run on client's data. Both parties need to keep their inputs private. We present new, more efficient privacy-protecting protocols for remote evaluation of such classification/diagnostic programs. In addition to efficiency improvements, we generalize previous solutions - we securely evaluate private linear branching programs (LBP), a useful generalization of BP that we introduce. We show practicality of our solutions: we apply our protocols to the privacy-preserving classification of medical ElectroCardioGram (ECG) signals and present implementation results. Finally, we discover and fix a subtle security weakness of the most recent remote diagnostic proposal, which allowed malicious clients to learn partial information about the program.