Addressing the Problems of Simple Anonymization Techniques Provide guarantees

Addressing the problems of simple anonymization

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Addressing the Problems of Simple Anonymization Techniques Provide guarantees that re-identification will not be possible within some bounds Eg: can only map a given individual to a set of 50 individuals 1. k-anonymization 2. l-diversity 3. t-closeness 4. Differential privacy 19
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Addressing Anonymization Problems: k-Anonymity A dataset has k-anonymity if at least k individuals share the same identifying values 20 k=2
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Addressing Anonymization Problems: k-Anonymity Problem: what if sensitive value is identical? 21 k=2
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Addressing Anomymization Problems: l-Diversity A dataset has l-diversity if the individuals that share the same identifying values have at least l distinct values for the sensitive attribute 22 l=3
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Addressing Anomymization Problems: t-Closeness A dataset has t-closeness if the individuals that share the same identifying values have values for the sensitive attribute that are within a threshold t of diversity 23
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Problems with Anonymization Techniques Taking an individual I off a dataset reveals their sensitive attribute information Eg: retrieving aggregate data before removal, then retrieving aggregate data after removal, and then comparing the difference will give us the sensitive attribute of I 24
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Differential Privacy Only method that provides mathematical guarantees of anonymity Differential privacy adds “noise” to the retrieval process so that such comparisons do not give us the actual sensitive attribute information 25
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Research Ethics Ethical treatment of research participants 26
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Institutional Review Board (IRB) Reviews research to ensure ethical treatment of human subjects Levels of review Full board Expedited Exempt Non-human subjects research (e.g., oral history)
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IRB considerations All personnel involved must be trained Data and identities must be protected Benefits must outweigh the risks Informed consent Participation must be voluntary Participants must be informed
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Driven by violations Nuremberg Military Tribunal Academic or government-sponsored research
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  • Fall '17
  • Statistics, Informed consent, Computational Thinking and Data Science

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