Secure Random Group Selection in Data Analytics
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Solution Overview
Problem
Existing privacy-preserving protocols for big data analytics face challenges in minimizing communication and computational complexity while maintaining security, especially when dealing with large user groups, leading to potential information leakage due to user collusion.
Innovation Solution
A method for secure random group selection and computation that involves client devices generating a random binary vector in an obfuscated domain, where bit values indicate selection, using homomorphic encryption or secret-sharing, reducing complexity to 3tN secure multiplications and ensuring secure computations without revealing the selected group or inputs to clients or servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random user selection is implemented using permutation matrices as described by Kononchuk et al., then security against user collusion is improved, but communication and computational complexity increases significantly
Solution Approach 1:
The patent segments the random selection process into two independent phases: (1) generating a random binary vector where each bit independently indicates user selection, and (2) using this vector for secure computation. This segmentation eliminates the need for complex permutation matrices while maintaining security, reducing computational complexity from O(N²) matrix operations to O(N) simple bit operations.
Solution Approach 2:
The patent extracts the essential security requirement from the Kononchuk approach - preventing collusion - while discarding the complex permutation mechanism. By using a simple random binary vector generated through secure multiparty computation, the patent achieves the same security goal with minimal computational overhead, suitable for resource-constrained mobile devices.
2Adaptability or versatility
If the number of users N is increased to handle large-scale data analytics, then the applicability and coverage of the system is improved, but the permutation process becomes increasingly complex and resource-intensive
Solution Approach 1:
The patent changes the fundamental parameter of random selection from permutation-based (requiring N! operations) to binary-vector-based (requiring only N bit operations). This parameter change allows the system to scale to large user groups (N in the millions) without increasing complexity, as each additional user simply adds one more bit to the selection vector rather than requiring a complete re-computation of permutations.
3Reliability
If cryptographic techniques are used to obfuscate privacy-sensitive data, then privacy protection is improved, but information leakage remains possible through repeated protocol execution with colluding users
Solution Approach 1:
The patent introduces dynamics into the selection process by generating a new random binary vector for each computation round through secure multiparty computation. This dynamic re-randomization ensures that even if users collude and repeat the protocol multiple times, they cannot predict or reconstruct the selection criteria, preventing information leakage while maintaining privacy protection throughout repeated executions.
Data Source
AI summary
Methods for secure random selection of t client devices from a set of N client devices and methods for secure computation of inputs of t client devices randomly selected from N client devices are described. Such random selection method may include determining an initial binary vector b of weight t by setting the first t bits to one: bi=1, 1≤i≤t, and all further bits to zero: bi=0, t<i≤N; each client device i (i=1, . . . , N) of the set of N client devices jointly generating a random binary vector b of weight t in an obfuscated domain on the basis of the initial binary vector b including: determining a position n in the binary vector; determining a random number r in {n, n+1, . . . N}; and, using the random number to swap binary values at positions n and r of the binary vector b.