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| Content Provider | ACM Digital Library |
|---|---|
| Author | Fan, Liyue Jin, Hongxia |
| Abstract | The availability of an increasing amount of user generated data is transformative to our society. We enjoy the benefits of analyzing big data for public interest, such as disease outbreak detection and traffic control, as well as for commercial interests, such as smart grid and product recommendation. However, the large collection of user generated data contains unique patterns and can be used to re-identify individuals, which has been exemplified by the AOL search log release incident. In this paper, we propose a practical framework for data analytics, while providing differential privacy guarantees to individual data contributors. Our framework generates differentially private aggregates which can be used to perform data mining and recommendation tasks. To alleviate the high perturbation errors introduced by the differential privacy mechanism, we present two methods with different sampling techniques to draw a subset of individual data for analysis. Empirical studies with real-world data sets show that our solutions enable accurate data analytics on a small fraction of the input data, reducing user privacy risk and data storage requirement without compromising the analysis results. |
| Starting Page | 311 |
| Ending Page | 321 |
| Page Count | 11 |
| File Format | |
| ISBN | 9781450334693 |
| DOI | 10.1145/2736277.2741122 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2015-05-18 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Differential privacy Data analytics Sampling |
| Content Type | Text |
| Resource Type | Article |
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