STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
A new public key cryptosystem based on higher residues
CCS '98 Proceedings of the 5th ACM conference on Computer and communications security
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Investigative Data Mining for Security and Criminal Detection
Investigative Data Mining for Security and Criminal Detection
Privacy preserving association rule mining in vertically partitioned data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
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
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Private Representative-Based Clustering for Vertically Partitioned Data
ENC '04 Proceedings of the Fifth Mexican International Conference in Computer Science
Privacy-Preserving Outlier Detection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Privately computing a distributed k-nn classifier
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Combinatorial Algorithms: Theory and Practice
Combinatorial Algorithms: Theory and Practice
Privacy Preserving Nearest Neighbor Search
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
The privacy of k-NN retrieval for horizontal partitioned data: new methods and applications
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
Privacy-preserving regression algorithms
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Protocols for secure computations
SFCS '82 Proceedings of the 23rd Annual Symposium on Foundations of Computer Science
Public-key cryptosystems based on composite degree residuosity classes
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Privacy preserving DBSCAN for vertically partitioned data
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
A Secure Protocol to Maintain Data Privacy in Data Mining
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Private predictions on hidden Markov models
Artificial Intelligence Review
Proceedings of the 9th annual ACM workshop on Privacy in the electronic society
A secure protocol for point-segment position problem
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
Bands of privacy preserving objectives: classification of PPDM strategies
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Verifying correctness of inner product of vectors in cloud computing
Proceedings of the 2013 international workshop on Security in cloud computing
An Enhanced Mobile-Healthcare Emergency System Based on Extended Chaotic Maps
Journal of Medical Systems
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Recently, privacy issues have become important in data analysis, especially when data is horizontally partitioned over several parties. In data mining, the data is typically represented as attribute-vectors and, for many applications, the scalar (dot) product is one of the fundamental operations that is repeatedly used. In privacy-preserving data mining, data is distributed across several parties. The efficiency of secure scalar products is important, not only because they can cause overhead in communication cost, but dot product operations also serve as one of the basic building blocks for many other secure protocols. Although several solutions exist in the relevant literature for this problem, the need for more efficient and more practical solutions still remains. In this paper, we present a very efficient and very practical secure scalar product protocol. We compare it to the most common scalar product protocols. We not only show that our protocol is much more efficient than the existing ones, we also provide experimental results by using a real life dataset.