Privacy-Preserving Distributed Data Mining Techniques: A Survey

Baby, V.; Chandra, Naveen

doi:10.5120/ijca2016910381

Cited by 11 publications

(3 citation statements)

References 21 publications

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“…Similar kind of work was found in [24]. Clifton et al [25] presented tool for PPDDM (Privacy Preserving Distributed Data Mining). The tools include secure multi-party computation, secure sum, secure set union, secure size of set intersection, and scalar product.…”

Section: Related Worksupporting

confidence: 55%

Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

Radhika

Kumari

2018

IJECE

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Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy serving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming.

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Section: Related Worksupporting

confidence: 55%

Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

Radhika

Kumari

2018

IJECE

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“…Privacy-Preserving Distributed Data Mining (PPDDM) focuses on distributed data analysis without leaking sensitive information from one party to another. PPDDM technology aims to make it technically or mathematically impossible to derive the original data from communication messages and even from the final analysis results [7], [8]. There are three issues in PPDDM for real-world applications.…”

Section: Introductionmentioning

confidence: 99%

A Reliable Application of MPC for Securing the Tri-Training Algorithm

Kurniawan

Mambo

2023

IEEE Access

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Due to the widespread use of distributed data mining techniques in a variety of areas, the issue of protecting the privacy of sensitive data has received increasing attention in recent years. Privacypreserving distributed data mining (PPDDM) focuses on decentralized data analysis without the disclosure of sensitive information from data owner. However, the previous PPDDM mostly works on a limited amount of labeled data. In contrast to the real world, unlabeled data is abundance and labeled data is scarce. The objectives of this paper are to study and to analyze privacy-preserving properties of semi-supervised learning (SSL) algorithm with the combination of labeled and unlabeled data, where data is distributed among multiple data owners. In this paper we propose a Privacy-preserving Distributed Data Mining (PPDDM) method by designing a reliable application of secure MPC to semi-supervised tri-training algorithms. We simulate the original tri-training algorithm and tri-training algorithm with secure MPC using a different types of classifiers and datasets. The simulation results show that tri-training in secure MPC has almost same accuracy compared to original tri-training algorithm. We also compare execution time in addition to performance evaluation of tri-training in secure and the original tri-training algorithms.

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“…Privacypreservingtechniquesareintroducedforavoidingsensitiveinformation(PawarandAnuradha,2020 ;Jadhav et al, 2016). Asymmetric and symmetric encryption are the traditional tools to protect privacy,andisbroadlyutilizedforpreventingtheunauthorizedusersaccessingsensitiveinformation transmitted through the networks (Baby and Chandra, 2016). Identifying anomalous behavior of thephysicalprocesswithrespecttofaultidentificationwherebothcyber-attacksandfaultsproduce anomalyinthephysicalprocess.Moreover,thepatternsofcyber-attacksaswellasfaultsaredifferent andhence,theattacksarenottreatedasfaultsintheCPS (Basile,et al,2006).TheIDSisclassifiedto anomaly-basedandsignature-basedapproaches (VeeraiahandKrishna,2018).Here,theanomalyand signature-enabledIndustrialIDS(IIDS)arecomplementary.However,theSignature-enabledIIDSs removeanddetecttheillegalpacketsintheCPSinwhichtheanomaly-basedIIDSsisutilizedtoextract normalbehaviorandtodetecttheanomaly (Garcia-Teodoro,et al,2009;Liu,2020).Nowadays, thenetworkintrusiondetectionapproachesareprogressedtothehighlysophisticatedlevels,which involvesadvancedsignalprocessingmethods,butthetimeseriesanalysis,wavelets,andtheprincipal componentanalysis,whencomparedtoothertechniques,arenotlimited.Hence,thebroadlyutilized detectorsarenon-signatureandsignaturefordetectingnetworkanomalies (Sadreazami,et al,2017).…”

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confidence: 99%

Adam Improved Rider Optimization-Based Deep Recurrent Neural Network for the Intrusion Detection in Cyber Physical Systems

Kamble

Malemath

2022

International Journal of Swarm Intelligence Research

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This paper designed the intrusion detection systems for determining the intrusions. Here, Adam Improved rider optimization approach (Adam IROA) is newly developed for detecting the intrusion in intrusion detection. Accordingly, the training of DeepRNN is done by proposed Adam IROA, which is designed by combining the Adam optimization algorithm with IROA. Thus, the newly developed Adam IROA is applied for intrusion detection. Overall, two phases are included in the proposed intrusion detection system, which involves feature selection and classification. Here, the features selection is done using proposed WWIROA to select significant features from the input data. The proposed WWIROA is developed by combining WWO and IROA. The obtained features are fed to the classification module for discovering the intrusions present in the network. Here, the classification is progressed using Adam IROA-based DeepRNN. The proposed Adam IROA-based DeepRNN achieves maximal accuracy of 0.937, maximal sensitivity of 0.952, and maximal specificity of 0.908 based on SCADA dataset.

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Privacy-Preserving Distributed Data Mining Techniques: A Survey

Cited by 11 publications

References 21 publications

Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

Misusability Measure Based Sanitization of Big Data for Privacy Preserving MapReduce Programming

A Reliable Application of MPC for Securing the Tri-Training Algorithm

Adam Improved Rider Optimization-Based Deep Recurrent Neural Network for the Intrusion Detection in Cyber Physical Systems

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