Unified Locational Differential Privacy Framework
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866917659177648128 |
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| author | Priyanshu, Aman Maurya, Yash Ganesh, Suriya Tran, Vy |
| author_facet | Priyanshu, Aman Maurya, Yash Ganesh, Suriya Tran, Vy |
| contents | Aggregating statistics over geographical regions is important for many applications, such as analyzing income, election results, and disease spread. However, the sensitive nature of this data necessitates strong privacy protections to safeguard individuals. In this work, we present a unified locational differential privacy (DP) framework to enable private aggregation of various data types, including one-hot encoded, boolean, float, and integer arrays, over geographical regions. Our framework employs local DP mechanisms such as randomized response, the exponential mechanism, and the Gaussian mechanism. We evaluate our approach on four datasets representing significant location data aggregation scenarios. Results demonstrate the utility of our framework in providing formal DP guarantees while enabling geographical data analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03903 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unified Locational Differential Privacy Framework Priyanshu, Aman Maurya, Yash Ganesh, Suriya Tran, Vy Artificial Intelligence Computers and Society Aggregating statistics over geographical regions is important for many applications, such as analyzing income, election results, and disease spread. However, the sensitive nature of this data necessitates strong privacy protections to safeguard individuals. In this work, we present a unified locational differential privacy (DP) framework to enable private aggregation of various data types, including one-hot encoded, boolean, float, and integer arrays, over geographical regions. Our framework employs local DP mechanisms such as randomized response, the exponential mechanism, and the Gaussian mechanism. We evaluate our approach on four datasets representing significant location data aggregation scenarios. Results demonstrate the utility of our framework in providing formal DP guarantees while enabling geographical data analysis. |
| title | Unified Locational Differential Privacy Framework |
| topic | Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2405.03903 |