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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhang, Yihua Frikken, Keith B. |
| Abstract | Recently many schemes, including k-anonymity [8], l-diversity [6] and t-closeness [5] have been introduced for preserving individual privacy when publishing database tables. Furthermore k-anonymity and l-diversity have been shown to have weaknesses. In this paper, we show that t-closeness also has limitations, more specifically we argue that: i) choosing the correct value for t is difficult, ii) t-closeness does not allow some values of sensitive attributes to be more sensitive than other values, and iii) to prevent certain types of privacy leaks t must be set to such a small value that it produces low-quality published data. In this paper we propose a new privacy $metric,(α_{i},$ $β_{i})-closeness,$ that mitigates these problems. We also show how to calculate an optimal release table (in the full domain model) that satisfies $(α_{i},$ $β_{i})-closeness$ and we present experimental results that show that the data quality provided by $9α_{i},$ $β;_{i}),-closeness$ is higher than t-closeness, k-anonymity, and l-diversity while achieving the same privacy goals. |
| Starting Page | 117 |
| Ending Page | 122 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781605582894 |
| DOI | 10.1145/1456403.1456423 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2008-10-27 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Privacy Data publishing Generalization |
| Content Type | Text |
| Resource Type | Article |
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