Count-Mean-Sketch Differential Privacy Vector Encoding
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Solution Overview
Problem
Current local differential privacy mechanisms are resource-intensive in terms of computational cost and transmission bandwidth, making them inefficient for processing and transmitting privatized user data in client-server environments.
Innovation Solution
The implementation of a differential privacy mechanism using the count-mean-sketch technique, which reduces resource requirements by encoding user data as a vector and privatizing it with a predefined probability, allowing for efficient frequency estimation while maintaining privacy guarantees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If local differential privacy mechanisms are used to protect user data, then privacy guarantees are improved, but computational cost and transmission bandwidth increase
Solution Approach 1:
The patent changes the parameters of the differential privacy mechanism by using a count-mean-sketch approach with randomized hashing and vector encoding. This transforms the traditional differential privacy implementation into a more efficient version that reduces computational cost while maintaining privacy guarantees through mathematical proofs.
Solution Approach 2:
The patent creates a simplified copy of the user data representation using vectors and hash functions. Instead of transmitting the full original data, it transmits encoded vector representations that preserve privacy properties while reducing computational and bandwidth requirements.
2Reliability
If local differential privacy mechanisms are used to protect user data, then privacy guarantees are improved, but transmission bandwidth increases
Solution Approach 1:
The patent extracts only the essential information needed for frequency estimation and transmits it in a compressed vector format. By taking out only the necessary data components and encoding them efficiently, it reduces transmission bandwidth while maintaining the privacy guarantees through the randomized hashing and vector privatization processes.
3Measurement precision
If traditional encoding methods are used for user data, then data accuracy is maintained, but computational complexity increases
Solution Approach 1:
The patent changes the encoding parameters by using count-mean-sketch with randomized hashing functions and vector representations. This approach maintains data accuracy for frequency estimation while reducing computation complexity through more efficient mathematical operations compared to traditional encoding methods.
Data Source
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AI summary
Embodiments described herein provide a privacy mechanism to protect user data when transmitting the data to a server that estimates a frequency of such data amongst a set of client devices. In one embodiment, a differential privacy mechanism is implemented using a count-mean-sketch technique that can reduce resource requirements required to enable privacy while providing provable guarantees regarding privacy and utility. For instance, the mechanism can provide the ability to tailor utility (e.g. accuracy of estimations) against the resource requirements (e.g. transmission bandwidth and computation complexity).