Anonymized noise addition in subspaces for privacy preserved data mining in high dimensional continuous data

Virupaksha, Shashidhar; Dondeti, Venkatesulu

doi:10.1007/s12083-021-01080-y

Cited by 12 publications

(5 citation statements)

References 39 publications

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“…Perturbation based privacy preservation methods proposed for data stream mining are Chamikara et al (2018), Martínez Rodríguez et al (2017, Virupaksha and Dondeti (2021), Denham et al (2020), Rajalakshmi and Mala (2013). In Chamikara et al (2018), "P2Ro-CAl", a combination of Condensation, rotation, and random swapping has been proposed for data stream classification.…”

Section: Ppdm Methods For Data Stream Miningmentioning

confidence: 99%

“…However, the noise addition filter has the risk of disclosure. An anonymization method based on noise addition for privacy preservation has been proposed in Virupaksha and Dondeti (2021) for clustering. This method chooses random noise within the subspace limits of the dense and non-dense subspaces to reduce information loss and enhance cluster identification.…”

Section: Ppdm Methods For Data Stream Miningmentioning

confidence: 99%

“…It uses the effect of logistic function to control the noise level but still adds it cumulatively to achieve a high accuracy level. Meanwhile, some interesting PPDM methods in data stream mining do not include any discussion about accuracy-privacy trade-off (Mohammadian et al 2014;Rajesh et al 2012;Mohamed et al 2017;Martínez Rodríguez et al 2017;Nyati et al 2018;Rajalakshmi and Mala 2013;Navarro-Arribas and Torra 2014;Virupaksha and Dondeti 2021;Wang et al 2018.…”

Section: Mean Absolute Errormentioning

confidence: 99%

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Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

2023

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This study investigates existing input privacy-preserving data mining (PPDM) methods and privacy-preserving data stream mining methods (PPDSM), including their strengths and weaknesses. A further analysis was carried out to determine to what extent existing PPDM/PPDSM methods address the trade-off between data mining accuracy and data privacy which is a significant concern in the area. The systematic literature review was conducted using data extracted from 104 primary studies from 5 reputed databases. The scope of the study was defined using three research questions and adequate inclusion and exclusion criteria. According to the results of our study, we divided existing PPDM methods into four categories: perturbation, non-perturbation, secure multi-party computation, and combinations of PPDM methods. These methods have different strengths and weaknesses concerning the accuracy, privacy, time consumption, and more. Data stream mining must face additional challenges such as high volume, high speed, and computational complexity. The techniques proposed for PPDSM are less in number than the PPDM. We categorized PPDSM techniques into three categories (perturbation, non-perturbation, and other). Most PPDM methods can be applied to classification, followed by clustering and association rule mining. It was observed that numerous studies have identified and discussed the accuracy-privacy trade-off. However, there is a lack of studies providing solutions to the issue, especially in PPDSM.

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Section: Ppdm Methods For Data Stream Miningmentioning

confidence: 99%

Section: Ppdm Methods For Data Stream Miningmentioning

confidence: 99%

Section: Mean Absolute Errormentioning

confidence: 99%

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Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

2023

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“…Cui proposed two parallel methods of decision tree, one based on synchronous tree and the other based on split tree [14]. Virupaksha and Dondeti proposed the implementation of parallel algorithm of decision tree classification algorithm in PVM system and realized this method through PVM [15]. Radhika and Masood proposed a parallel algorithm of decision tree classification algorithm based on MPI.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Decision Tree Algorithm Based on Data Mining in English Teaching Evaluation

Hou

2022

Wireless Communications and Mobile Computing

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In order to bring new opportunities and challenges to teaching and learning English. This paper proposes a study of the use of a data tree-based decision tree algorithm in English language learning assessment. Teaching and learning through online English learning platforms using data mining technology to analyze student learning data, create relevant models, and explore the relationship between English exams and various elements is important for student learning and teacher teaching. Firstly, this paper introduces the function, process, and common machine learning models of data mining. Finally, by using data created by college English tutors and measurements to create and identify student images, through image users, you can learn about student behavior, learning behaviors, etc., provide a foundation after the design experiment, technology training technology in logistic regression model, wooden model making, and postfusion modeling of two models and analyze the factors affecting students’ passing the exam according to the prediction results. The results show that the decision tree model predicts that the score of each question type occurs 72 times, the score of students’ examination occurs 70 times, and the completion of homework occurs 70 times. Trees determined by this approach can have a profound impact on a wide range of indicators of academic excellence and provide a fundamental and useful basis for measuring future improvement.

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“…Privacy Metric-Matching /reidentification of data: Each algorithm in this review has its performance measure of privacy. K-anonymity primarily uses entropy to quantify information loss [3], while in differential privacy using k-means, disclosure risk is used to identify before and after adding noise to a cluster [16,17]. The dummy location success can also be measured using entropy.…”

Section: Performance Metrics and Their Measurementmentioning

confidence: 99%

Obfuscation-Based Mechanisms in Location-Based Privacy Protection

Wozny

Pan

Anwar

2022

Studies in Computational Intelligence

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Protection of personal data is vital to individuals. With the prevalence of cyber-crime, it is important to leverage the power of artificial intelligence (AI) to protect the user. The more someone else knows about a person and their whereabouts, the more power they can have over that person. This power takes the form of influence over their decisions, perhaps reveals to others their behaviours, where they live, and subsequently can affect their reputation or even safety. This review will provide an exploration into three methods of obscuring a person's geographical location when using location-based services. It will explain to the reader how artificial intelligence can be used in their favour. Unfortunately, there is no perfect protection of privacy because location-based services (LBS) rely on the device's accurate location to provide services, such as restaurant recommendations or games like Pokemon. But we should at least aim for a favourable balance between total privacy and the exposure of information on an individual. Three obfuscation-based mechanisms were compared on their data privacy efficacy, data utility, and ease of implementation. These mechanisms are: location generalisation (cloaking), location perturbation (differential privacy), and location spoofing (using dummy locations). Gowalla is a location-based social networking website where users share their locations by checking-in. A subset of the Gowalla dataset was used to evaluate these three mechanisms. In this work, three obfuscation methodologies for locationbased services to protect the user's location and privacy were evaluated. For the Gowalla dataset subset in London, the differential privacy using logistic regression with an epsilon of 0.8 seemed to have the best performance in terms of privacy, utility and overhead. k-Anonymity came second in its reasonable performance across all three metrics. t-Closeness obfuscation performed better in privacy, but worse than l-diversity in utility. Finally, these were followed by the application of dummy based locations. This method was the simplest but was the least refined in its ability to hide user datapoints and the most vulnerable to malicious attacks. The differential privacy

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Anonymized noise addition in subspaces for privacy preserved data mining in high dimensional continuous data

Cited by 12 publications

References 39 publications

Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

Privacy-preserving data (stream) mining techniques and their impact on data mining accuracy: a systematic literature review

Application of Decision Tree Algorithm Based on Data Mining in English Teaching Evaluation

Obfuscation-Based Mechanisms in Location-Based Privacy Protection

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