Data Anonymity Meets Non-discrimination

Ruggieri, Salvatore

doi:10.1109/icdmw.2013.56

Cited by 3 publications

(4 citation statements)

References 15 publications

(35 reference statements)

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“…The relation of post-processing approach (knowledge publishing) and anti-discrimination is described by . Ruggieri (2013) has specified the impact of the privacy-preserving model, t-closeness on anti-discrimination. He has developed a method to make the data both discrimination-free and privacy protected.…”

Section: Relationship Between Privacy-preserving Data Mining (Ppdm)mentioning

confidence: 99%

Discrimination-aware data mining: a survey

Kashid

Kulkarni

Patankar

2017

IJDS

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Data mining is a very important and useful technique to extract knowledge from raw data. However, there is a challenge faced by data mining researchers, in the form of potential discrimination. Discrimination means giving unfair treatment to a person just because one belongs to a minority group, without considering one's individual merit or qualification. The results extracted using data mining techniques may lead to discrimination, if a biased historical/training dataset is used. It is very important to prevent data mining technique from becoming a source of discrimination. A detailed survey of discrimination discovery methods and discrimination prevention methods is presented in this paper. This paper also presents the list of datasets used for experiments in different discrimination-aware data mining (DADM) approaches. Some ideas for future research work that may help in preventing discrimination are also discussed.

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Section: Relationship Between Privacy-preserving Data Mining (Ppdm)mentioning

confidence: 99%

Discrimination-aware data mining: a survey

Kashid

Kulkarni

Patankar

2017

IJDS

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“…The method to make data discrimination free using privacy preserving model (e.g. t-closeness) is shown in [20]. The impact of knowledge publishing on anti-discrimination is shown in [21] [22].…”

Section: Related Workmentioning

confidence: 99%

“…to develop a preprocessing technique which will handle both the problems faced by data mining-potential privacy violation and potential discrimination. Although some work is already done to identify effect of data anonymization on anti-discrimination [20] [21] [22], there are many other privacy preserving techniques such as permutation, perturbation, bucketization, slicing [24] which can be used for discrimination prevention. Slicing allows less information loss than that of generalization; it preserves both privacy and correlation in the original data.…”

Section: Proposed Workmentioning

confidence: 99%

Discrimination Prevention using Privacy Preserving Techniques

Kashid¹,

Kulkarni²,

Patankar³

2015

IJCA

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Recently, it is observed that data mining technique may come across two problems-potential discrimination and potential privacy violation. Discrimination occurs as a result of use of discriminatory datasets for data mining tasks. Privacy violation occurs if a person's sensitive information is displayed to an unauthorized entity as a result of data mining tasks. Use of privacy preserving techniques to make data privacy protected can affect the amount of discrimination caused. It is important to study the relation of privacy and discrimination in the context of data mining. In this paper, we are trying to propose a method in which privacy preserving technique can be used to prevent discrimination and we can make the original data both privacy protected and discrimination-free.

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“…The method to make data discrimination free using privacy preserving model (e.g. t-closeness) is depicted in [13]. The effect of knowledge publishing on antidiscrimination is shown in [14] [15].…”

Section: Related Workmentioning

confidence: 99%

A System to Analyze an Effect of Privacy Protection on Direct Discrimination

Gorave¹,

Kulkarni²

2017

IJCA

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Recently, it is observed that data mining technique comes across two major potential risks from social perspective: discrimination and privacy violation. Discrimination means treating people unfairly, just because they belong to minority group, without taking into account their individual qualification. Data mining technique undergoes risk of discrimination, if data mining tasks are performed using discriminatory dataset. Discrimination Prevention Data Mining is an area, which deals with discovering, preventing and measuring discrimination. Privacy provides right to a person to decide whether to disclose or not to disclose his/her sensitive information. Privacy violation occurs if a person's sensitive information is disclosed as a result of data mining tasks. Privacy

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Data Anonymity Meets Non-discrimination

Cited by 3 publications

References 15 publications

Discrimination-aware data mining: a survey

Discrimination-aware data mining: a survey

Discrimination Prevention using Privacy Preserving Techniques

A System to Analyze an Effect of Privacy Protection on Direct Discrimination

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