Function Secret Sharing for Private Data Retrieval
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Solution Overview
Problem
Existing technologies fail to ensure user privacy in data retrieval from remote databases, as they often require revealing queries to the data source, and lack efficient methods for searching over encrypted data without decryption.
Innovation Solution
The implementation of a function secret sharing (FSS) scheme combined with conditional disclosure of secrets (CDS) allows a user to decompose a function into shares distributed across multiple servers, enabling private evaluation of functions on remote data sets without revealing the function or data accessed, using shared randomness and a secret known to at least one server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional data retrieval methods are used, then data accessibility is improved, but user privacy is worsened as queries must be revealed to the data source
Solution Approach 1:
The patent applies function secret sharing to decompose a query function into multiple function shares that are distributed to different servers. Each server executes its function share on the data set independently, and only the final Boolean result is reconstructed from the individual shares. This segmentation allows data retrieval without revealing the complete query function to any single server, thus maintaining user privacy while enabling data accessibility.
Solution Approach 2:
The patent introduces function shares as intermediary objects that mediate between the user and the data set. Instead of directly querying the data source with a visible query function, the user sends encrypted function shares to servers, which execute them and return encrypted results. The user then reconstructs the final result from these intermediary shares, achieving private data retrieval without direct query exposure to the data source.
2Loss of information
If encrypted data storage is implemented, then data privacy is improved, but search efficiency is worsened without decryption
Solution Approach 1:
The patent segments the search function into function shares that can be executed independently on encrypted data. Each function share processes a portion of the search query against the encrypted data set, and the results are combined to produce the final search outcome. This allows efficient searching over encrypted data without requiring full decryption, as each server can perform local computations on encrypted data and only the final result needs to be decrypted by the user.
3Loss of information
If function secret sharing is implemented, then query privacy is improved, but system complexity is worsened
Solution Approach 1:
The patent segments the complex query function into simpler function shares that can be independently executed by multiple servers. Each function share is a simplified version of the original query that can be processed in parallel on encrypted data. This segmentation reduces the computational complexity at each server while maintaining the overall query privacy, as the complex logic is distributed rather than concentrated at a single point.
Data Source
AI summary
A function is decomposed into a plurality of function shares. The function returns a Boolean result based on whether an input y satisfies a query on a data set. The function shares hide the function from non-collaborating entities that separately execute the function shares. Each of the functions shares are sent to one of a plurality of servers having a same data set. The function shares are executed on the data set at the servers to obtain a respective plurality of shares. A conditional disclosure of secrets operation is simulated on the shares and the input y. The conditional disclosure of secrets operation uses a secret known to at least one of the servers, and further uses a source of randomness shared between the servers. A Boolean value corresponding to the Boolean result is returned based on the conditional disclosure of secrets operation returning the secret.