STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
How to prove yourself: practical solutions to identification and signature problems
Proceedings on Advances in cryptology---CRYPTO '86
Completeness theorems for non-cryptographic fault-tolerant distributed computation
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
PODC '97 Proceedings of the sixteenth annual ACM symposium on Principles of distributed computing
Simplified VSS and fast-track multiparty computations with applications to threshold cryptography
PODC '98 Proceedings of the seventeenth annual ACM symposium on Principles of distributed computing
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Multiparty Computation with Faulty Majority
CRYPTO '89 Proceedings of the 9th Annual International Cryptology Conference on Advances in Cryptology
Fair Computation of General Functions in Presence of Immoral Majority
CRYPTO '90 Proceedings of the 10th Annual International Cryptology Conference on Advances in Cryptology
Non-Interactive and Information-Theoretic Secure Verifiable Secret Sharing
CRYPTO '91 Proceedings of the 11th Annual International Cryptology Conference on Advances in Cryptology
Zero-Knowledge Proofs for Finite Field Arithmetic; or: Can Zero-Knowledge be for Free?
CRYPTO '98 Proceedings of the 18th Annual International Cryptology Conference on Advances in Cryptology
General Adversaries in Unconditional Multi-party Computation
ASIACRYPT '99 Proceedings of the International Conference on the Theory and Applications of Cryptology and Information Security: Advances in Cryptology
Trapdooring Discrete Logarithms on Elliptic Curves over Rings
ASIACRYPT '00 Proceedings of the 6th International Conference on the Theory and Application of Cryptology and Information Security: Advances in Cryptology
Revealing information while preserving privacy
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Foundations of Cryptography: Volume 2, Basic Applications
Foundations of Cryptography: Volume 2, Basic Applications
Privacy-preserving Bayesian network structure computation on distributed heterogeneous data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
Smooth sensitivity and sampling in private data analysis
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Fairplay—a secure two-party computation system
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mechanism Design via Differential Privacy
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
A learning theory approach to non-interactive database privacy
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
Scalable Multiparty Computation with Nearly Optimal Work and Resilience
CRYPTO 2008 Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology
Distributed Private Data Analysis: Simultaneously Solving How and What
CRYPTO 2008 Proceedings of the 28th Annual conference on Cryptology: Advances in Cryptology
Implementing Two-Party Computation Efficiently with Security Against Malicious Adversaries
SCN '08 Proceedings of the 6th international conference on Security and Cryptography for Networks
FairplayMP: a system for secure multi-party computation
Proceedings of the 15th ACM conference on Computer and communications security
Differentially private recommender systems: building privacy into the net
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 18th ACM conference on Information and knowledge management
Perfectly-secure MPC with linear communication complexity
TCC'08 Proceedings of the 5th conference on Theory of cryptography
Ask a better question, get a better answer a new approach to private data analysis
ICDT'07 Proceedings of the 11th international conference on Database Theory
Algebraic geometric secret sharing schemes and secure multi-party computations over small fields
CRYPTO'06 Proceedings of the 26th annual international conference on Advances in Cryptology
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
An architectural solution for data exchange in cooperative network security research
Proceedings of the First Workshop on Building Analysis Datasets and Gathering Experience Returns for Security
Privacy-preserving statistical analysis on ubiquitous health data
TrustBus'11 Proceedings of the 8th international conference on Trust, privacy and security in digital business
Proceedings of the 4th ACM workshop on Security and artificial intelligence
Sedic: privacy-aware data intensive computing on hybrid clouds
Proceedings of the 18th ACM conference on Computer and communications security
Efficient Protocols for Principal Eigenvector Computation over Private Data
Transactions on Data Privacy
Differentially private iterative synchronous consensus
Proceedings of the 2012 ACM workshop on Privacy in the electronic society
Proceedings of the 2012 ACM conference on Computer and communications security
SplitX: high-performance private analytics
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Towards privacy-preserving fault detection
Proceedings of the 9th Workshop on Hot Topics in Dependable Systems
SPARSI: partitioning sensitive data amongst multiple adversaries
Proceedings of the VLDB Endowment
Proceedings of the First International Workshop on Middleware for Cloud-enabled Sensing
Computer Networks: The International Journal of Computer and Telecommunications Networking
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In this paper we introduce a framework for privacy-preserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy (P4P) and features a novel heterogeneous architecture and a number of efficient tools for performing private computation and ensuring security at large scale. It maintains the following properties: (1) Provably strong privacy; (2) Adequate efficiency at reasonably large scale; and (3) Robustness against realistic adversaries. The framework gains its practicality by decomposing data mining algorithms into a sequence of vector addition steps that can be privately evaluated using a new verifiable secret sharing (VSS) scheme over small field (e.g., 32 or 64 bits), which has the same cost as regular, non-private arithmetic. This paradigm supports a large number of statistical learning algorithms including SVD, PCA, k-means, ID3, EM-based machine learning algorithms, etc., and all algorithms in the statistical query model [36]. As a concrete example, we show how singular value decomposition (SVD), which is an extremely useful algorithm and the core of many data mining tasks, can be done efficiently with privacy in P4P. Using real-world data and actual implementation we demonstrate that P4P is orders of magnitude faster than existing solutions.