Making Privacy-preserving Federated Graph Analytics with Strong Guarantees Practical (for Certain Queries)
<|reference_start|>Making Privacy-preserving Federated Graph Analytics with Strong Guarantees Practical (for Certain Queries): 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.<|reference_end|>
@article{liu2024making,
    title={Making Privacy-preserving Federated Graph Analytics with Strong
  Guarantees Practical (for Certain Queries)},
    author={Kunlong Liu, Trinabh Gupta},
    journal={arXiv preprint arXiv:2404.01619},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01619},
    primaryClass={cs.CR cs.SI}
}