Privacy-Preserving Two-Party Distributed Association Rules Mining on Horizontally Partitioned Data

Zhang, Feng; Rong, Chunming; Zhao, Gansen; Wu, Jinxia; Wu, Xiangning

doi:10.1109/cloudcom-asia.2013.87

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…Feng Zhang et al [41] discussed privacy-preserving two-party DARM on horizontally partitioned data by encryption, done among peers for secure division. Experimental results revealed increased computation cost and communication cost than conventional approaches.…”

Section: Papyrusmentioning

confidence: 99%

Approaches and Techniques of Distributed Data Mining : A Comprehensive Study

Urmela¹,

Dr.M.²

2017

IJET

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-Distributed Data Mining (DDM) has become one of the promising areas of Data Mining (DM). DDM evolved from DM from the urge to mine data from distributed sites. DM paved way for increased computational cost and privacy due to centralized data mining, whereas DDM paved way for decrease in computational cost as well as enhanced data privacy by distributing resources across distributed sites. Mining techniques framed for DM can't be applied for DDM since mining DDM follows a different strategy compared to DM. DDM includes classifier based, agent based and privacy preserving based approaches. In this paper, DDM approaches and techniques is studied in detail. Keyword -Distributed Data Mining, distributed sites, computation cost, classifier approach, agent based, privacy-preserving I. INTRODUCTION Data Mining (DM) is the process of extracting useful information from datasets using DM techniques, namely pattern matching, clustering, rule association, regression, etc. The progressive growth of information technology has paved way to further explore Distributed/Collective Data Mining, Spatial and Geographic Data Mining, Temporal Data Mining, Spatio-Temporal Data Mining, Multimedia Data Mining and Phenomenal Data Mining. DM today performs computation on the database or warehouse at a single geographical location paving way for increased computation cost and questioning on data privacy. Future scope of DM is computing data located at different geographical locations. This is termed DDM/CDM (Collective Data Mining) [1].The main factors which led to the evolution of DDM are -privacy of sensitive data, transmission cost, computation cost and memory cost. The objective of DDM is to extract useful information from data located at heterogeneous sites. Distributed computing comprises distributed sites, hosting computing units at individual heterogeneous points. DDM follows decentralized mining strategy which differs from centralized strategy making entire working system scalable by distributing workload across heterogeneous sites [1].Alfredo Cuzzocrea[2] stated that framing a methodology for DDM is challenging not only by distributed environment, but also for its efficient resource sharing and minimized computational complexity specifications. DDM mainly comprises of two variations -data distributed and computation distributed. In the former method, data is distributed among heterogeneous sites at local level and computation is hosted at global level. In the latter method, computation is distributed among heterogeneous sites at local level and data is hosted at global level. Figure 1 explains DDM working architecture. The database of heterogeneous sites hosts useful, unknown information. DDM algorithms will be applied over data at heterogeneous sites as local model and finally the DM computed result will be agglomerated to form global model [1]. Kargupta et al.[3] and Zaki et al. [4] discussed that several researchers analyzed the complexity involved in framing methodology for DDM in two ways: analyzing on effective and...

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Section: Papyrusmentioning

confidence: 99%

Approaches and Techniques of Distributed Data Mining : A Comprehensive Study

Urmela¹,

Dr.M.²

2017

IJET

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“…To deal with this challenge, many researchers focus on the study of Privacy Preserving Data Mining (PPDM) technique. For example, a lot of work has been done in [47,17,50,2,41,44,30,11,52,21,22], which aim to provide a means to address the privacy preserving issue without accessing the actual data values, thus avoid the disclosure of information beyond the final results.…”

mentioning

confidence: 99%

Privacy preserving feature selection and Multiclass Classification for horizontally distributed data

Lu¹,

Yan²,

Han³

et al. 2018

Mathematical Foundations of Computing

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In the last two decades, a lot of scientific fields have experienced a huge growth in data volume and data complexity, which brings data miners lots of opportunities, as well as many challenges. With the advent of the era of big data, applying data mining techniques on assembling data from multiple parties (or sources) has become a leading trend. However, those data mining tasks may divulge individuals' privacy, which leads to the increased concerns in privacy preserving. In this work, a Privacy Preserving feature selection method (PPFS-IFW) and Multiclass Classification method (PPM2C) are proposed. Experiments had been conducted to validate the performance of the proposed approaches. Both PPFS-IFW and PPM2C were tested on six benchmark datasets. The testing results demonstrate PPFS-IFW's capability in enhancing the classification performance at the level of accuracy by selection informative features. PPFS-IFW can not only preserve private information but also outperform some other state-of-the-art feature selection approaches. Experimental results also show that the proposed PPM2C method is workable and stable. Particularly, It reduces the risk of over-fitting when compared with the regular Support Vector Machine. In the meantime, by employing the Secure Sum Protocol to encrypt data at the bottom layer, users' privacy is preserved.

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“…The integrated dataset will be used by the data miner to acquire information through queries [36] [32] [8]. For clarity, 'multiparty' in the data mining perspective usually refers to at least two or more parties.…”

Section: Privacy-preserving Data Processingmentioning

confidence: 99%

Secure MultiParty Protocol for Differentially-Private Data Release

Harris¹

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Privacy-Preserving Two-Party Distributed Association Rules Mining on Horizontally Partitioned Data

Cited by 7 publications

References 31 publications

Approaches and Techniques of Distributed Data Mining : A Comprehensive Study

Approaches and Techniques of Distributed Data Mining : A Comprehensive Study

Privacy preserving feature selection and Multiclass Classification for horizontally distributed data

Secure MultiParty Protocol for Differentially-Private Data Release

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