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ClaMPP: a cloud-based multi-party privacy preserving classification scheme for distributed applications

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Abstract

Privacy preservation in a distributed environment is a challenging task as it requires efficient control strategies for authentication and integrity preservation of various users and applications. In a distributed environment, most of the data items are distributed among various parties which may be located across different geographical locations. In the literature, most of the techniques proposed for privacy preserving consider only two parties collaboration for data items sharing using data perturbation and homomorphic encryption technique. Since data perturbation is less accurate and homomorphic encryption incurs high computation cost, so to fill these gaps, this paper proposes a Cloud-based Multi-party Privacy Preserving Classification Scheme (ClaMPP), which uses Naive Bayesian Classifier using multi-party random masking and polynomial aggregation technique for privacy preservation among different parties. The proposed ClaMPP scheme consists of four protocols which are described as follows. Protocols I and II are used to calculate conditional probabilities between the item vectors in a secure manner by using Privacy Preserving Conditional Probability Protocol. In the protocol III, the prior probabilities between the item vectors are computed by using additive property of Paillier homomorphic encryption technique. The naive Bayesian classification model constructed from the calculated values from protocols I, II and III is used in protocol IV to generate the predictions for items based on user’s interest. Three datasets MovieLens100K, MovieLens20M and Jester are used in ClaMPP to analyse the privacy, accuracy and overhead costs incurred. It has been experimentally demonstrated that the proposed scheme is secure, and the privacy preservation does not affect the accuracy of the prediction. Comparative analysis of ClaMPP has been done with other related schemes based on off-line model computation cost. It has been demonstrated from the results obtained that computation cost decreases significantly in comparison with the other state-of-art techniques.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar University, Patiala, Punjab, India

    Harmanjeet Kaur, Neeraj Kumar & Shalini Batra

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  1. Harmanjeet Kaur
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  2. Neeraj Kumar
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  3. Shalini Batra
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Correspondence to Neeraj Kumar.

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Kaur, H., Kumar, N. & Batra, S. ClaMPP: a cloud-based multi-party privacy preserving classification scheme for distributed applications. J Supercomput 75, 3046–3075 (2019). https://doi.org/10.1007/s11227-018-2691-0

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  • DOI: https://doi.org/10.1007/s11227-018-2691-0

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