Privacy-preserving Bayesian network structure computation on distributed heterogeneous data

Wright, Rebecca N.; Yang, Zhiqiang

doi:10.1145/1014052.1014145

Cited by 179 publications

(123 citation statements)

References 14 publications

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“…This line of research uses secure multi-party computation (SMC) protocols to protect private information [17,18]. What is not addressed by SMC studies is how much private information is actually disclosed by the computation results.…”

Section: Related Workmentioning

confidence: 99%

Understanding Privacy Risk of Publishing Decision Trees

Zhu

2010

Lecture Notes in Computer Science

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Abstract. Publishing decision trees can provide enormous benefits to the society. Meanwhile, it is widely believed that publishing decision trees can pose a potential risk to privacy. However, there is not much investigation on the privacy consequence of publishing decision trees. To understand this problem, we need to quantitatively measure privacy risk.Based on the well-established maximum entropy theory, we have developed a systematic method to quantify privacy risks when decision trees are published. Our method converts the knowledge embedded in decision trees into equations and inequalities (called constraints), and then uses nonlinear programming tool to conduct maximum entropy estimate. The estimate results are then used to quantify privacy. We have conducted experiments to evaluate the effectiveness and performance of our method.

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Section: Related Workmentioning

confidence: 99%

Understanding Privacy Risk of Publishing Decision Trees

Zhu

2010

Lecture Notes in Computer Science

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“…The work most related to ours is [12], where Wright and Yang applied homomorphic encryption [10] to the Bayesian networks induction for the case of two parties. However, the core protocol which is called Scalar Product Protocol can be easily attacked.…”

Section: Privacy-preserving Data Miningmentioning

confidence: 99%

“…In this paper, we develop a secure two-party protocol and a secure multi-party protocol based on homomorphic encryption. Our contribution not only overcomes the attacks which exist in [12], but more importantly, a general secure protocol involving multiple parties is provided.…”

Section: Privacy-preserving Data Miningmentioning

confidence: 99%

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Privacy-Preserving Collaborative Association Rule Mining

Zhan

Matwin

Chang

2005

Data and Applications Security XIX

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Abstract. This paper introduces a new approach to a problem of data sharing among multiple parties, without disclosing the data between the parties. Our focus is data sharing among parties involved in a data mining task. We study how to share private or confidential data in the following scenario: multiple parties, each having a private data set, want to collaboratively conduct association rule mining without disclosing their private data to each other or any other parties. To tackle this demanding problem, we develop a secure protocol for multiple parties to conduct the desired computation. The solution is distributed, i.e., there is no central, trusted party having access to all the data. Instead, we define a protocol using homomorphic encryption techniques to exchange the data while keeping it private.

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“…However, cryptographic methods may restrict data access and exchange too much. Furthermore, cryptographic privacy-preserving methods [15,22,24] usually tailor some specific data mining tasks, and therefore lose generality.…”

Section: Introductionmentioning

confidence: 99%

Achieving k-Anonymity by Clustering in Attribute Hierarchical Structures

Wong

et al. 2006

Data Warehousing and Knowledge Discovery

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Abstract. Individual privacy will be at risk if a published data set is not properly deidentified. k-anonymity is a major technique to de-identify a data set. A more general view of k-anonymity is clustering with a constraint of the minimum number of objects in every cluster. Most existing approaches to achieving k-anonymity by clustering are for numerical (or ordinal) attributes. In this paper, we study achieving k-anonymity by clustering in attribute hierarchical structures. We define generalisation distances between tuples to characterise distortions by generalisations and discuss the properties of the distances. We conclude that the generalisation distance is a metric distance. We propose an efficient clustering-based algorithm for k-anonymisation. We experimentally show that the proposed method is more scalable and causes significantly less distortions than an optimal global recoding k-anonymity method.

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Privacy-preserving Bayesian network structure computation on distributed heterogeneous data

Cited by 179 publications

References 14 publications

Understanding Privacy Risk of Publishing Decision Trees

Understanding Privacy Risk of Publishing Decision Trees

Privacy-Preserving Collaborative Association Rule Mining

Achieving k-Anonymity by Clustering in Attribute Hierarchical Structures

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