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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.21859 |
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| _version_ | 1866912859609366528 |
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| author | Taylor, Sophie Vippathalla, Praneeth Kumar Coon, Justin |
| author_facet | Taylor, Sophie Vippathalla, Praneeth Kumar Coon, Justin |
| contents | The fundamental trade-off between privacy and utility remains an active area of research. Our contribution is motivated by two observations. First, privacy mechanisms developed for one-time data release cannot straightforwardly be extended to sequential releases. Second, practical databases are likely to be useful to multiple distinct parties. Furthermore, we can not rule out the possibility of data sharing between parties. With utility in mind, we formulate a privacy-utility trade-off problem to adaptively tackle sequential data requests made by different, potentially colluding entities. We consider both expected distortion and mutual information as measures to quantify utility, and use mutual information to measure privacy. We assume an attack model whereby illicit data sharing, which we call collusion, can occur between data receivers. We develop an adaptive algorithm for data releases that makes use of a modified Blahut-Arimoto algorithm. We show that the resulting data releases are optimal when expected distortion quantifies utility, and locally optimal when mutual information quantifies utility. Finally, we discuss how our findings may extend to applications in machine learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21859 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Privacy of Sequential Data Releases Under Collusion Taylor, Sophie Vippathalla, Praneeth Kumar Coon, Justin Information Theory The fundamental trade-off between privacy and utility remains an active area of research. Our contribution is motivated by two observations. First, privacy mechanisms developed for one-time data release cannot straightforwardly be extended to sequential releases. Second, practical databases are likely to be useful to multiple distinct parties. Furthermore, we can not rule out the possibility of data sharing between parties. With utility in mind, we formulate a privacy-utility trade-off problem to adaptively tackle sequential data requests made by different, potentially colluding entities. We consider both expected distortion and mutual information as measures to quantify utility, and use mutual information to measure privacy. We assume an attack model whereby illicit data sharing, which we call collusion, can occur between data receivers. We develop an adaptive algorithm for data releases that makes use of a modified Blahut-Arimoto algorithm. We show that the resulting data releases are optimal when expected distortion quantifies utility, and locally optimal when mutual information quantifies utility. Finally, we discuss how our findings may extend to applications in machine learning. |
| title | Adaptive Privacy of Sequential Data Releases Under Collusion |
| topic | Information Theory |
| url | https://arxiv.org/abs/2601.21859 |