Privacy Preserving Distributed Association Rule Hiding Using Concept Hierarchy

Modak, Masooda

doi:10.1016/j.procs.2016.03.126

Cited by 6 publications

(4 citation statements)

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“…M. Modak and R. Shaikh observe that while data mining is an important and useful emerging trend, the possibility of it being distributed among various parties raises the issue of privacy [37]. According to X. Liao and C. Shu, nowadays, trust management is a new security problem that cannot be solved by traditional techniques such as data backup, recovery backup, and firewalls but by employing certain data hiding methods [38].…”

Section: Related Workmentioning

confidence: 99%

Distributed Spatial Search Using Paillier Cryptosystem and a Distributed Ring Algorithm

2022

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The problem of lack of anonymity and confidentiality can be experienced by those who collect statistical data online as well as those who provide the data. One end may be secure, for example, the one providing data, and yet the other end, for example, the one collecting data, may not be secure. In another scenario, both the data provider and collector may seek anonymity. Preventing the decryption of data provided while providing aggregated results is the best solution for such scenarios. To achieve this, this paper proposes a protocol that puts into application. Homomorphic Encryption and a Distributed Ring algorithm, to ensure data anonymity of both parties involved in a spatial search that is a data provider and a searcher. Firstly, we identify a Homomorphic Encryption technique that can work best for a spatial search by reviewing literature on Homomorphic Encryption techniques. Among the Homomorphic Encryption techniques reviewed were Rivest, Shamir and Adleman (RSA), El Gamal cryptosystem, Goldwasser-Micali cryptosystem, Benaloh cryptosystem, Paillier cryptosystem and Fully Homomorphic Encryption (FHE). After a comprehensive study, Paillier Homomorphic Encryption technique was identified as the best approach to be employed in securing a spatial search. Secondly, we propose a protocol for distributed spatial searching using Paillier cryptosystem and distributed ring algorithm principles. Finally, a proof of concept prototype using the proposed approach was implemented. From initial experiments conducted using the proposed approach, it is evident that the bigger cost comes from the communication over the network and less from the encryption algorithm and protocol itself. A 39.7% overhead when compared to the usefulness of the approach, is outweighed making the solution highly practical and useful.

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Section: Related Workmentioning

confidence: 99%

Distributed Spatial Search Using Paillier Cryptosystem and a Distributed Ring Algorithm

2022

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“…Privacy-preserving DDM success relies on building a valid DDM model for finding useful data associations but hiding the data from others. Mainly privacy preserving DDM model is built with classification, clustering and ARM [38].…”

Section: Papyrusmentioning

confidence: 99%

“…Though randomization technique is a better approach it suffers from accuracy when privacy is at its peak, but cryptographic technique provided better accuracy and privacy than randomization technique. Privacy preserving DDM is particularly applicable in almost all mining areas, namely clustering, ARM, bayesian model, decision tree, ensemble methods and CF [38].…”

Section: Papyrusmentioning

confidence: 99%

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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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Data Preprocess

Hossen

2020

Machine Learning and Big Data

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Privacy Preserving Distributed Association Rule Hiding Using Concept Hierarchy

Cited by 6 publications

References 5 publications

Distributed Spatial Search Using Paillier Cryptosystem and a Distributed Ring Algorithm

Distributed Spatial Search Using Paillier Cryptosystem and a Distributed Ring Algorithm

Approaches and Techniques of Distributed Data Mining : A Comprehensive Study

Data Preprocess

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