Making Privacy-preserving Federated Graph Analytics with Strong Guarantees Practical (for Certain Queries)
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911823800827904 |
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| author | Liu, Kunlong Gupta, Trinabh |
| author_facet | Liu, Kunlong Gupta, Trinabh |
| contents | Privacy-preserving federated graph analytics is an emerging area of research. The goal is to run graph analytics queries over a set of devices that are organized as a graph while keeping the raw data on the devices rather than centralizing it. Further, no entity may learn any new information except for the final query result. For instance, a device may not learn a neighbor's data. The state-of-the-art prior work for this problem provides privacy guarantees for a broad set of queries in a strong threat model where the devices can be malicious. However, it imposes an impractical overhead: each device locally requires over 8.79 hours of cpu time and 5.73 GiBs of network transfers per query. This paper presents Colo, a new, low-cost system for privacy-preserving federated graph analytics that requires minutes of cpu time and a few MiBs in network transfers, for a particular subset of queries. At the heart of Colo is a new secure computation protocol that enables a device to securely and efficiently evaluate a graph query in its local neighborhood while hiding device data, edge data, and topology data. An implementation and evaluation of Colo shows that for running a variety of COVID-19 queries over a population of 1M devices, it requires less than 8.4 minutes of a device's CPU time and 4.93 MiBs in network transfers - improvements of up to three orders of magnitude. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01619 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Making Privacy-preserving Federated Graph Analytics with Strong Guarantees Practical (for Certain Queries) Liu, Kunlong Gupta, Trinabh Cryptography and Security Social and Information Networks Privacy-preserving federated graph analytics is an emerging area of research. The goal is to run graph analytics queries over a set of devices that are organized as a graph while keeping the raw data on the devices rather than centralizing it. Further, no entity may learn any new information except for the final query result. For instance, a device may not learn a neighbor's data. The state-of-the-art prior work for this problem provides privacy guarantees for a broad set of queries in a strong threat model where the devices can be malicious. However, it imposes an impractical overhead: each device locally requires over 8.79 hours of cpu time and 5.73 GiBs of network transfers per query. This paper presents Colo, a new, low-cost system for privacy-preserving federated graph analytics that requires minutes of cpu time and a few MiBs in network transfers, for a particular subset of queries. At the heart of Colo is a new secure computation protocol that enables a device to securely and efficiently evaluate a graph query in its local neighborhood while hiding device data, edge data, and topology data. An implementation and evaluation of Colo shows that for running a variety of COVID-19 queries over a population of 1M devices, it requires less than 8.4 minutes of a device's CPU time and 4.93 MiBs in network transfers - improvements of up to three orders of magnitude. |
| title | Making Privacy-preserving Federated Graph Analytics with Strong Guarantees Practical (for Certain Queries) |
| topic | Cryptography and Security Social and Information Networks |
| url | https://arxiv.org/abs/2404.01619 |