Discrimination Prevention using Privacy Preserving Techniques
TLDR
This paper is trying to propose a method in which privacy preserving technique can be used to prevent discrimination and the original data can be made both privacy protected and discrimination-free.Abstract:
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.read more
Citations
More filters
Privacy for All: Ensuring Fair and Equitable Privacy Protections
TL;DR: This position paper argues for applying recent research on ensuring sociotechnical systems are fair and nondiscriminatory to the privacy protections those systems may provide to help explain the disparate impact of privacy failure.
Posted Content
Stable and Fair Classification
TL;DR: In this paper, the authors proposed an extended framework based on fair classification algorithms that are formulated as optimization problems, by introducing a stability-focused regularization term, which can be used to inform the selection of the regularization parameter in their framework.
Journal ArticleDOI
Discrimination Prevention with Classification and Privacy Preservation in Data mining
TL;DR: This work has shown that data mining and automated data collection techniques such as classification and association rule mining have provided way to taking decisions automatically, such as computation of insurance premium, loan granting or denial, credit card issue etc.
Journal ArticleDOI
Privacy in computer ethics: Navigating the digital age
TL;DR: In this paper , the authors discuss five key points related to privacy in computer ethics: the concept of privacy and its significance in the context of computer ethics; ethical considerations surrounding personal information in the digital space, including issues of consent, transparency, and data protection; the legal framework surrounding privacy in different jurisdictions, such as data protection laws and international standards; the role of technology in protecting privacy, including the use of encryption and other security measures; and finally, the challenges associated with protecting privacy in the Digital Age.
References
More filters
Journal ArticleDOI
Data preprocessing techniques for classification without discrimination
Faisal Kamiran,Toon Calders +1 more
TL;DR: This paper surveys and extends existing data preprocessing techniques, being suppression of the sensitive attribute, massaging the dataset by changing class labels, and reweighing or resampling the data to remove discrimination without relabeling instances and presents the results of experiments on real-life data.
Journal ArticleDOI
Three naive Bayes approaches for discrimination-free classification
Toon Calders,Sicco Verwer +1 more
TL;DR: Three approaches for making the naive Bayes classifier discrimination-free are presented: modifying the probability of the decision being positive, training one model for every sensitive attribute value and balancing them, and adding a latent variable to the Bayesian model that represents the unbiased label and optimizing the model parameters for likelihood using expectation maximization.
Proceedings ArticleDOI
Discrimination-aware data mining
TL;DR: This approach leads to a precise formulation of the redlining problem along with a formal result relating discriminatory rules with apparently safe ones by means of background knowledge, and an empirical assessment of the results on the German credit dataset.