Shuffled Differential Privacy Aggregation with Intermediary Shuffler
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Solution Overview
Problem
Current differential privacy models face challenges in achieving high accuracy and low communication overhead while ensuring privacy, with the central model relying on strong trust assumptions and the local model incurring significant accuracy loss, and the shuffled model needing to balance between the two.
Innovation Solution
The proposed method employs infinitely divisible distributions for sampling random values to generate multisets of messages, which are then aggregated, allowing for privacy-preserving data summation and histogram estimation with minimal communication overhead and accuracy close to central DP algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the central model of DP is used, then high accuracy is achieved, but strong trust assumptions are required
Solution Approach 1:
The patent introduces a trusted shuffler as an intermediary component between users and the curator. The shuffler randomly permutes all incoming messages from users before passing them to the analyzer, serving as a mediator that enables the system to achieve central model accuracy without requiring the curator to directly trust users' raw data. This intermediary structure transforms the trust relationship while maintaining high measurement precision.
2Reliability
If the local model of DP is used, then minimal trust assumptions are made, but substantial accuracy loss occurs
Solution Approach 1:
By introducing the shuffler intermediary, the system bridges the gap between local and central models. The shuffler's random permutation of messages allows the curator to process aggregated data with central model efficiency while users maintain local model privacy guarantees, thus recovering accuracy without sacrificing trust minimization.
Solution Approach 2:
The patent segments the differential privacy system into three distinct components: local randomizers at user devices, a trusted shuffler, and a central analyzer. This segmentation allows each component to operate under different trust assumptions, with the shuffler acting as a boundary that separates local privacy guarantees from central processing efficiency, thereby resolving the accuracy-trust contradiction.
3Measurement precision
If more messages are transmitted per user, then higher accuracy is achieved, but communication overhead increases
Solution Approach 1:
The patent merges multiple message transmissions into a single aggregated multiset that is shuffled and transmitted once. By combining the randomization, shuffling, and aggregation operations, the system achieves high accuracy through central model processing while minimizing communication overhead to essentially one message per user, eliminating the need for multiple separate transmissions.
Data Source
AI summary
The present disclosure provides practical communication-efficient and low-error algorithms for aggregation of private data. For example, the proposed algorithms can be implemented in the shuffled DP model. Specific example operations that can be performed using the proposed algorithms include summation (e.g., binary summation, integer summation) and histograms over a moderate number of buckets. The proposed algorithms achieve accuracy that is arbitrarily close to that of central DP algorithms with an expected communication per user essentially matching what is needed without any privacy constraints.