Introducing efficient parallelism into approximate string matching and a new serial algorithm
STOC '86 Proceedings of the eighteenth annual ACM symposium on Theory of computing
Applied cryptography (2nd ed.): protocols, algorithms, and source code in C
Applied cryptography (2nd ed.): protocols, algorithms, and source code in C
A new public key cryptosystem based on higher residues
CCS '98 Proceedings of the 5th ACM conference on Computer and communications security
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Efficient private bidding and auctions with an oblivious third party
CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
The String-to-String Correction Problem
Journal of the ACM (JACM)
Bounds on the Complexity of the Longest Common Subsequence Problem
Journal of the ACM (JACM)
Bounds for the String Editing Problem
Journal of the ACM (JACM)
A Cost-Effective Pay-Per-Multiplication Comparison Method for Millionaires
CT-RSA 2001 Proceedings of the 2001 Conference on Topics in Cryptology: The Cryptographer's Track at RSA
Speeding Up Secret Computations with Insecure Auxiliary Devices
CRYPTO '88 Proceedings of the 8th Annual International Cryptology Conference on Advances in Cryptology
Security and Performance of Server-Aided RSA Computation Protocols
CRYPTO '95 Proceedings of the 15th Annual International Cryptology Conference on Advances in Cryptology
Fast Server-Aided RSA Signatures Secure Against Active Attacks
CRYPTO '95 Proceedings of the 15th Annual International Cryptology Conference on Advances in Cryptology
Secure and private sequence comparisons
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
Attacks on protocols for server-aided RSA computation
EUROCRYPT'92 Proceedings of the 11th annual international conference on Theory and application of cryptographic techniques
Private Information: To Reveal or not to Reveal
ACM Transactions on Information and System Security (TISSEC)
Secure outsourcing of DNA searching via finite automata
DBSec'10 Proceedings of the 24th annual IFIP WG 11.3 working conference on Data and applications security and privacy
Point-based trust: define how much privacy is worth
ICICS'06 Proceedings of the 8th international conference on Information and Communications Security
Secure and verifiable outsourcing of sequence comparisons
ICT-EurAsia'13 Proceedings of the 2013 international conference on Information and Communication Technology
Data-oblivious graph algorithms for secure computation and outsourcing
Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security
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Large-scale problems in the physical and life sciences are being revolutionized by Internet computing technologies, like grid computing, that make possible the massive cooperative sharing of computational power, bandwidth, storage, and data. A weak computational device, once connected to such a grid, is no longer limited by its slow speed, small amounts of local storage, and limited bandwidth: It can avail itself of the abundance of these resources that is available elsewhere on the network. An impediment to the use of “computational outsourcing” is that the data in question is often sensitive, e.g., of national security importance, or proprietary and containing commercial secrets, or to be kept private for legal requirements such as the HIPAA legislation, Gramm-Leach-Bliley, or similar laws. This motivates the design of techniques for computational outsourcing in a privacy-preserving manner, i.e., without revealing to the remote agents whose computational power is being used, either one's data or the outcome of the computation on the data. This paper investigates such secure outsourcing for widely applicable sequence comparison problems, and gives an efficient protocol for a customer to securely outsource sequence comparisons to two remote agents, such that the agents learn nothing about the customer's two private sequences or the result of the comparison. The local computations done by the customer are linear in the size of the sequences, and the computational cost and amount of communication done by the external agents are close to the time complexity of the best known algorithm for solving the problem on a single machine (i.e., quadratic, which is a huge computational burden for the kinds of massive data on which such comparisons are made). The sequence comparison problem considered arises in a large number of applications, including speech recognition, machine vision, and molecular sequence comparisons. In addition, essentially the same protocol can solve a larger class of problems whose standard dynamic programming solutions are similar in structure to the recurrence that subtends the sequence comparison algorithm.