Differential Privacy for the Vast Majority
| Autor: | Hasan B. Kartal, Xiaoping Liu, Xiao-Bai Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
021110 strategic
defence & security studies Measure (data warehouse) Information privacy Information retrieval General Computer Science Computer science 0211 other engineering and technologies Pareto principle Relaxation (iterative method) 02 engineering and technology Article Management Information Systems Information sensitivity Database query 0202 electrical engineering electronic engineering information engineering Information system Differential privacy 020201 artificial intelligence & image processing |
| Popis: | Differential privacy has become one of the widely used mechanisms for protecting sensitive information in databases and information systems. Although differential privacy provides a clear measure of privacy guarantee, it implicitly assumes that each individual corresponds to a single record in the result of a database query. This assumption may not hold in many database query applications. When an individual has multiple records, strict implementation of differential privacy may cause significant information loss. In this study, we extend the differential privacy principle to situations where multiple records in a database are associated with the same individual. We propose a new privacy principle that integrates differential privacy with the Pareto principle in analyzing privacy risk and data utility. When applied to the situations with multiple records per person, the proposed approach can significantly reduce the information loss in the released query results with a relatively small relaxation in the differential privacy guarantee. The effectiveness of the proposed approach is evaluated using three real-world databases. |
| Databáze: | OpenAIRE |
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