@inproceedings{hajian_study_2012,
	title = {A {Study} on the {Impact} of {Data} {Anonymization} on {Anti}-discrimination},
	isbn = {978-1-4673-5164-5 978-0-7695-4925-5},
	url = {http://ieeexplore.ieee.org/document/6406462/},
	doi = {10.1109/ICDMW.2012.19},
	abstract = {In last years, data mining has raised some concerns related to privacy invasion of the individuals and potential discrimination based on the extracted patterns and profiles. Efforts at fighting against these risks have led to developing privacy preserving data mining (PPDM) techniques and anti-discrimination techniques in data mining. However, there is an evident gap between the large body of research
in data privacy technologies and the recent early results on anti-discrimination technologies. This context presents a study on the relation between data anonymization from privacy technologies literature and anti-discrimination. We discuss how different data anonymization techniques have impact on
discriminatory biased datasets.},
	urldate = {2016-12-05},
	publisher = {IEEE},
	author = {Hajian, Sara and Domingo-Ferrer, Josep},
	month = dec,
	year = {2012},
	keywords = {FATML.org Bibliography, DADM, privacy},
	pages = {352--359},
	annote = {Author Keywords
Privacy; Anti-discrimination; Data anonymization; Generalization; Suppression; Classification rule
 
Reading Notes

Orients discrimination within a legal/legislative framework, which has progressed parallel to privacy legislation.
References privacy preserving data mining (PPDM), a term not commonly seen thus far in the literature. PPDM incorporates anonymizing records in ways that minimize the distortion to the data caused by minimization. 
PPDM is somewhat superseded by discrimination prevention in data mining (DPDM), another term that is somewhat novel here. It consists of transforming "data in such a way that the discriminatory biases contained in the original data are removed so that no unfair decision models can be mined from the transformed data".
This article addresses the relationship between data anonymization and anti-discrimination. They ask:

Can protecting privacy achieve anti-discrimination?
Can anonymization techniques be adapted to prevent discrimination?
Can anonymization techniques be adapted to protect against both privacy and discrimination risks?


The authors conclude that the above questions can only be satisfied if anti-discrimination is taken into account at the anonymization stage, and that anonymization does not lend itself to preventing discrimination
},
	file = {Hajian-AStudyontheImpactofDataAnonymizationonAntidiscrimination.pdf:C\:\\Users\\Ashudeep Singh\\Zotero\\storage\\V96ZAXTD\\Hajian-AStudyontheImpactofDataAnonymizationonAntidiscrimination.pdf:application/pdf}
}

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Efforts at fighting against these risks have led to developing privacy preserving data mining (PPDM) techniques and anti-discrimination techniques in data mining. However, there is an evident gap between the large body of research\nin data privacy technologies and the recent early results on anti-discrimination technologies. This context presents a study on the relation between data anonymization from privacy technologies literature and anti-discrimination. We discuss how different data anonymization techniques have impact on\ndiscriminatory biased datasets.},\n\turldate = {2016-12-05},\n\tpublisher = {IEEE},\n\tauthor = {Hajian, Sara and Domingo-Ferrer, Josep},\n\tmonth = dec,\n\tyear = {2012},\n\tkeywords = {FATML.org Bibliography, DADM, privacy},\n\tpages = {352--359},\n\tannote = {Author Keywords\nPrivacy; Anti-discrimination; Data anonymization; Generalization; Suppression; Classification rule\n \nReading Notes\n\nOrients discrimination within a legal/legislative framework, which has progressed parallel to privacy legislation.\nReferences privacy preserving data mining (PPDM), a term not commonly seen thus far in the literature. PPDM incorporates anonymizing records in ways that minimize the distortion to the data caused by minimization. \nPPDM is somewhat superseded by discrimination prevention in data mining (DPDM), another term that is somewhat novel here. 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