Privacy preserving ID3 using Gini Index over horizontally partitioned data

Samet, Saeed; Miri, Ali

doi:10.1109/aiccsa.2008.4493598

Cited by 49 publications

(41 citation statements)

References 14 publications

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“…A protocol has been proposed for this purpose in [9], which uses additive homomorphic encryption [5]. However that protocol is not secure against collusion attack in the multi-party case.…”

Section: Secure Multi-party Multiplicationmentioning

confidence: 99%

“…Here, we propose another protocol to prevent that type of attack. The algorithm for the two-party case is the same as that in [9] briefly described in Appendix A, and the multi-party case is presented here. Suppose E i is an additive homomorphic encryption established by p i , with public key e i and private key d i .…”

Section: Secure Multi-party Multiplicationmentioning

confidence: 99%

“…This protocol, with the same lack of security against collusion attack, is proposed in [9]. Here, a protocol is presented to counter collusion attacks.…”

Section: Secure Multi-party Additionmentioning

confidence: 99%

“…Here, a protocol is presented to counter collusion attacks. The algorithm for the two-party case is the same as that in [9] and is briefly shown in Appendix B.…”

Section: Secure Multi-party Additionmentioning

confidence: 99%

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Incremental learning of privacy-preserving Bayesian networks

Samet

Miri

Granger

2013

Applied Soft Computing

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Bayesian Networks (BNs) have received significant attention in various academic and industrial applications, such as modeling knowledge in image processing, engineering, medicine and bio-informatics. Preserving the privacy of sensitive data, owned by different parties, is often a critical issue. However, in many practical applications, BNs must train from data that gradually becomes available at different period of times, on which the traditional batch learning algorithms are not suitable or applicable. In this paper, an algorithm based on a new and efficient version of Sufficient Statistics is proposed for incremental learning with BNs. The standard K2 algorithm is also modified to be utilized inside the incremental learning algorithm. Next, some secure building blocks such as secure multi-party multiplication, comparison, and factorial, which are resistant against colluding attacks and could be applied securely over public channels like internet, are presented to be used inside the main protocol. Then a privacy-preserving protocol is proposed for incremental learning of BNs, in which the structure and probabilities are estimated incrementally from homogeneously distributed and gradually available data among two or multi-parties. Finally, security and complexity analyses along with the experimental results are presented to compare with the batch algorithm and to show its performance

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“…A protocol has been proposed for this purpose in [9], which uses additive homomorphic encryption [5]. However that protocol is not secure against collusion attack in the multi-party case.…”

Section: Secure Multi-party Multiplicationmentioning

confidence: 99%

Section: Secure Multi-party Multiplicationmentioning

confidence: 99%

“…This protocol, with the same lack of security against collusion attack, is proposed in [9]. Here, a protocol is presented to counter collusion attacks.…”

Section: Secure Multi-party Additionmentioning

confidence: 99%

“…Here, a protocol is presented to counter collusion attacks. The algorithm for the two-party case is the same as that in [9] and is briefly shown in Appendix B.…”

Section: Secure Multi-party Additionmentioning

confidence: 99%

See 2 more Smart Citations

Incremental learning of privacy-preserving Bayesian networks

Samet

Miri

Granger

2013

Applied Soft Computing

Self Cite

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show abstract

“…Methods for privacy preserving association rule mining in distributed environments were proposed by Kantarcioglu and Clifton [12]. [7][11] [13] Constructs a classifier model using secure multiparty protocols. Classification using neural networks and preserving privacy is discussed in [14] [15][16] [20].…”

Section: Introductionmentioning

confidence: 99%