Efficient Privacy-Preserving Federated Graph Analytics for Certain Queries

Belangrijkste concepten

Colo, a new system, enables efficient privacy-preserving federated graph analytics for a subset of queries by using tailored secure computation and metadata-hiding communication protocols.

Samenvatting

The paper presents Colo, a new system for privacy-preserving federated graph analytics. Colo targets a subset of graph queries that have predicates with a limited set of inputs and outputs, and that evaluate these predicates between a device and its neighbors and then aggregate the results across the devices.
Colo's workflow consists of three phases:

Query distribution: The analyst submits a query to the servers, who validate it and distribute it to the devices.
Local aggregation: Each device evaluates the query in its local neighborhood using a new secure computation protocol that hides node, edge, and topology data. Devices communicate via a metadata-hiding network to protect topology information.
Global aggregation: Devices secret share their local results with servers, who aggregate them and send the final result to the analyst.

Colo's key innovations are:

A tailored secure computation protocol that operates over a limited set of inputs and outputs, making it more efficient than general-purpose protocols.
Leveraging the metadata-hiding Karaoke system to enable devices to communicate anonymously and hide their topology.
A simple global aggregation protocol that ensures honest devices' results are aggregated exactly once.
Colo's evaluation shows that for 1M devices, it requires less than 8.4 minutes of device cpu time and 4.93 MiB of network transfers per query, which is up to three orders of magnitude better than the state-of-the-art Mycelium system.

Statistieken

For 1M devices connected to up to 50 neighbors each:

Colo's per-device cost is less than 8.4 minutes of (single core) cpu time and 4.93 MiB of network transfers.
Colo's server-side cost is $3.95 to $37.6 per server ($158 to $1,504 total for 40 servers), depending on the query.

Citaten

None.

Belangrijkste Inzichten Gedestilleerd Uit

Making Privacy-preserving Federated Graph Analytics with Strong Guarantees Practical (for Certain Queries)

by Kunlong Liu,... om arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01619.pdf

Diepere vragen

How can Colo's techniques be extended to support a broader class of graph queries beyond the limited set considered in this paper

To extend Colo's techniques to support a broader class of graph queries beyond the limited set considered in the paper, several approaches can be taken. One way is to enhance the preprocessing step to handle a wider range of query types. By refining the PreProcess function to accommodate more complex transformations of raw data into query attributes, Colo can be adapted to handle a broader set of queries. Additionally, expanding the local aggregation protocol to incorporate more sophisticated secure computation techniques, such as advanced zero-knowledge proofs or multi-party computation, can enable Colo to process a wider variety of queries while maintaining privacy guarantees. Furthermore, optimizing the metadata-hiding communication system, like Karaoke, to efficiently handle a larger volume of messages and diverse query structures would be beneficial in supporting a broader class of graph queries.

How can Colo's security guarantees be strengthened to ensure integrity in the presence of malicious parties, not just privacy

To strengthen Colo's security guarantees to ensure integrity in the presence of malicious parties, not just privacy, several measures can be implemented. One approach is to incorporate robust integrity checks at each phase of the protocol, including query distribution, local aggregation, and global aggregation. By introducing cryptographic mechanisms like digital signatures and hash functions, Colo can verify the authenticity and integrity of messages exchanged between devices and servers. Additionally, implementing redundancy and error detection mechanisms can help detect and mitigate any attempts at data tampering or manipulation by malicious parties. Furthermore, enhancing the secure computation protocols with additional verification steps and redundancy can bolster the system's resilience against integrity threats.

What are the potential applications of Colo's privacy-preserving federated graph analytics beyond the infectious disease scenario discussed in the paper

The potential applications of Colo's privacy-preserving federated graph analytics extend far beyond the infectious disease scenario discussed in the paper. Some notable applications include:

Financial Services: Colo can be utilized in the financial sector for analyzing transaction networks, detecting fraudulent activities, and ensuring compliance with regulatory requirements while preserving the privacy of sensitive financial data.
Supply Chain Management: Colo can aid in analyzing supply chain networks, optimizing logistics operations, and identifying inefficiencies or vulnerabilities in the supply chain while protecting the confidentiality of proprietary information.
Social Network Analysis: Colo can be applied to study social networks, identify influential nodes, analyze information diffusion patterns, and detect communities within social graphs without compromising the privacy of individual users.
Smart Cities: Colo can support urban planning by analyzing transportation networks, energy grids, and public services utilization to optimize resource allocation and enhance sustainability efforts while safeguarding the privacy of citizen data.
Healthcare Analytics: Apart from infectious disease tracking, Colo can be used in healthcare for analyzing patient data, studying disease spread patterns, and optimizing treatment strategies while maintaining patient privacy and confidentiality.