| Title | Randomized Bit Vector: Privacy-Preserving Encoding Mechanism |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Sun, Lin, Zhang, Lan, Ye, Xiaojun |
| Conference Name | Proceedings of the 27th ACM International Conference on Information and Knowledge Management |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-6014-2 |
| Keywords | compositionality, data anonymization, Data Sanitization, Differential privacy, Human Behavior, privacy, privacy-preserving record linkage, pubcrawl, pufferfish mechanism, resilience |
| Abstract | Recently, many methods have been proposed to prevent privacy leakage in record linkage by encoding record pair data into another anonymous space. Nevertheless, they cannot perform well in some circumstances due to high computational complexities, low privacy guarantees or loss of data utility. In this paper, we propose distance-aware encoding mechanisms to compare numerical values in the anonymous space. We first embed numerical values into Hamming space by a low-computational encoding algorithm with randomized bit vector. To provide rigorous privacy guarantees, we use the random response based on differential privacy to keep global indistinguishability of original data and use Laplace noises via pufferfish mechanism to provide local indistinguishability. Besides, we provide an approach for embedding and privacy-related parameters selection to improve data utility. Experiments on datasets from different data distributions and application contexts validate that our approaches can be used efficiently in privacy-preserving record linkage tasks compared with previous works and have excellent performance even under very small privacy budgets. |
| URL | http://doi.acm.org/10.1145/3269206.3271703 |
| DOI | 10.1145/3269206.3271703 |
| Citation Key | sun_randomized_2018 |
Randomized Bit Vector: Privacy-Preserving Encoding Mechanism