Personalized Anonymization for Set-Valued Data by Partial Suppression
| Autor: | Hiromi Arai, Takuma Nakagawa, Hiroshi Nakagawa |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Information privacy
Computer science Probabilistic logic Inference 02 engineering and technology Data publishing computer.software_genre Field (computer science) Data modeling 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Personally identifiable information computer |
| Zdroj: | ICDM Workshops |
| DOI: | 10.1109/icdmw.2017.142 |
| Popis: | Set-valued data is comprised of records that are sets of items, such as goods purchased by each individual. Methods of publishing and widely utilizing set-valued data while protecting personal information have been extensively studied in the field of privacy-preserving data publishing. Until now, basic models such as k-anonymity or km-anonymity could not cope with attribute inference by an adversary with background knowledge of the records. On the other hand, the ρ-uncertainty model makes it possible to prevent attribute inference with a confidence value above a certain level in set-valued data. However, even in that case, there is the problem that items to be protected have to be designated in advance. In this research, we propose a new model that can provide more suitable privacy protection for each individual by protecting different items designated for each record distinctively and build a heuristic algorithm to achieve this guarantee using partial suppression. In addition, considering the problem that the computational complexity of the algorithm increases combinatorially with increasing data size, we introduce the concept of probabilistic relaxation of privacy guarantee. Finally, we show the experimental results of evaluating the performance of the algorithms using real-world datasets. |
| Databáze: | OpenAIRE |
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