Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Handbook of Applied Cryptography
Handbook of Applied Cryptography
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A Secure Protocol for Computing Dot-Products in Clustered and Distributed Environments
ICPP '02 Proceedings of the 2002 International Conference on Parallel Processing
Privacy-Preserving Cooperative Statistical Analysis
ACSAC '01 Proceedings of the 17th Annual Computer Security Applications Conference
On the Privacy Preserving Properties of Random Data Perturbation Techniques
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data
IEEE Transactions on Knowledge and Data Engineering
Deriving private information from randomized data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Privacy Preserving Data Mining (Advances in Information Security)
Privacy Preserving Data Mining (Advances in Information Security)
Secure set intersection cardinality with application to association rule mining
Journal of Computer Security
Preserving privacy in association rule mining with bloom filters
Journal of Intelligent Information Systems
On private scalar product computation for privacy-preserving data mining
ICISC'04 Proceedings of the 7th international conference on Information Security and Cryptology
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
Bloom filter bootstrap: privacy-preserving estimation of the size of an intersection
DBSec'13 Proceedings of the 27th international conference on Data and Applications Security and Privacy XXVII
EsPRESSO: Efficient privacy-preserving evaluation of sample set similarity
Journal of Computer Security
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The secure scalar product (or dot product) is one of the most used sub-protocols in privacy-preserving data mining. Indeed, the dot product is probably the most common sub-protocol used. As such, a lot of attention has been focused on coming up with secure protocols for computing it. However, an inherent problem with these protocols is the extremely high computation cost --- especially when the dot product needs to be carried out over large vectors. This is quite common in vertically partitioned data, and is a real problem. In this paper, we present ways to efficiently compute the approximate dot product. We implement the dot product protocol and demonstrate the quality of the approximation. Our dot product protocol can be used to securely and efficiently compute association rules from data vertically partitioned between two parties.