Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Leveraging the "Multi" in secure multi-party computation
Proceedings of the 2003 ACM workshop on Privacy in the electronic society
Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data
Proceedings of the 2006 ACM symposium on Applied computing
Tools for privacy preserving Kernel methods in data mining
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
The motivation and proposition of a privacy-enhancing architecture for operational databases
ACSW '07 Proceedings of the fifth Australasian symposium on ACSW frontiers - Volume 68
Privacy-preserving collision detection of two circles
Proceedings of the 2nd international conference on Scalable information systems
A new efficient privacy-preserving scalar product protocol
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Anonymity preserving pattern discovery
The VLDB Journal — The International Journal on Very Large Data Bases
Privacy-preserving Naïve Bayes classification
The VLDB Journal — The International Journal on Very Large Data Bases
Preservation of Privacy in Thwarting the Ballot Stuffing Scheme
TrustBus '08 Proceedings of the 5th international conference on Trust, Privacy and Security in Digital Business
An Efficient Approximate Protocol for Privacy-Preserving Association Rule Mining
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Impossibility of unconditionally secure scalar products
Data & Knowledge Engineering
The impact of data obfuscation on the accuracy of collaborative filtering
Expert Systems with Applications: An International Journal
More on shared-scalar-product protocols
ISPEC'06 Proceedings of the Second international conference on Information Security Practice and Experience
Approximate privacy-preserving data mining on vertically partitioned data
DBSec'12 Proceedings of the 26th Annual IFIP WG 11.3 conference on Data and Applications Security and Privacy
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Do I know you?: efficient and privacy-preserving common friend-finder protocols and applications
Proceedings of the 29th Annual Computer Security Applications Conference
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Dot-products form the basis of various applications ranging from scientific computations to commercial applications in data mining and transaction processing. Typical scientific computations utilizing sparse iterative solvers use repeated matrix-vector products. These canbe viewed as dot-products of sparse vectors. In database applications, dot-products take the form of counting operations. With widespread use of clustered and distributed platforms, these operations are increasingly being performed across networked hosts. Traditional APIs for messaging are susceptible to sniffing, and the data being transferred between hosts is often enough to compromise the entire computation. For example, in a domain decomposition based sparse solver, the entire solution can often be reconstructed easily from boundary values that are communicated on the net. In yet other applications, dot-products may be performed across two hosts that do not want to disclose their vectors, yet, they need to compute the dot-product. In each of these cases, there is a need for secure and anonymous dot-productprotocols. Due to the large computational requirements of underlying applications, it is highly desirable that secure protocols add minimal overhead to the original algorithm. Finally, by its very nature, dot-products leak limited amounts of information 驴 one of the parties can detect an entry of the other party's vector by simply probing it with a vector with a 1 in a particular location and zeros elsewhere. Given all of these constraints, traditional cryptographic protocols are generally unsuitable due to their significant computational and communication overheads. In this paper, we present an extremely efficient and sufficiently secure protocol for computing the dot-product of two vectors using linear algebraic techniques. Using analytical as well as experimental results, we demonstrate superior performance in terms of computational overhead, numerical stability, and security. We show that the overhead of a two-party dot-product computation using MPI as the messaging API across two high-end workstations connected via a Gigabit ethernet approaches multiple 4.69 over an unsecured dot-product. We also show that the average relative error in dot-products across a large number of random (normalized) vectors was roughly 4:5 脳 10-9.