Seeding with Differentially Private Network Information
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917104133865472 |
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| author | Liu, Yuxin Rahimian, M. Amin Yu, Fang-Yi |
| author_facet | Liu, Yuxin Rahimian, M. Amin Yu, Fang-Yi |
| contents | In public health interventions such as distributing preexposure prophylaxis (PrEP) for HIV prevention, decision makers often use seeding algorithms to identify key individuals who can amplify intervention impact. However, building a complete sexual activity network is typically infeasible due to privacy concerns. Instead, contact tracing can provide influence samples, observed sequences of sexual contacts, without full network reconstruction. This raises two challenges: protecting individual privacy in these samples and adapting seeding algorithms to incomplete data. We study differential privacy guarantees for influence maximization when the input consists of randomly collected cascades. Building on recent advances in costly seeding, we propose privacy-preserving algorithms that introduce randomization in data or outputs and bound the privacy loss of each node. Theoretical analysis and simulations on synthetic and real-world sexual contact data show that performance degrades gracefully as privacy budgets tighten, with central privacy regimes achieving better trade-offs than local ones. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_16590 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Seeding with Differentially Private Network Information Liu, Yuxin Rahimian, M. Amin Yu, Fang-Yi Social and Information Networks Computational Complexity Multiagent Systems Probability Applications 91D30, 05C80 In public health interventions such as distributing preexposure prophylaxis (PrEP) for HIV prevention, decision makers often use seeding algorithms to identify key individuals who can amplify intervention impact. However, building a complete sexual activity network is typically infeasible due to privacy concerns. Instead, contact tracing can provide influence samples, observed sequences of sexual contacts, without full network reconstruction. This raises two challenges: protecting individual privacy in these samples and adapting seeding algorithms to incomplete data. We study differential privacy guarantees for influence maximization when the input consists of randomly collected cascades. Building on recent advances in costly seeding, we propose privacy-preserving algorithms that introduce randomization in data or outputs and bound the privacy loss of each node. Theoretical analysis and simulations on synthetic and real-world sexual contact data show that performance degrades gracefully as privacy budgets tighten, with central privacy regimes achieving better trade-offs than local ones. |
| title | Seeding with Differentially Private Network Information |
| topic | Social and Information Networks Computational Complexity Multiagent Systems Probability Applications 91D30, 05C80 |
| url | https://arxiv.org/abs/2305.16590 |