Hybrid LDP Encoding for Accuracy and Lower Communication Cost
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Solution Overview
Problem
Existing data transmission methods lack an effective balance between data privacy and communication resource usage, with unary encoding providing high accuracy but high costs, and hash-based encoding reducing costs but compromising accuracy due to hash collisions.
Innovation Solution
A hybrid combination of unary and hash-based encoding techniques is applied to data subsets, where unary encoding is used for the most accessed data items and hash-based encoding for the rest, with local differential privacy mechanisms to manage tradeoffs and enhance security and reduce communication costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If unary encoding is used for all data items, then measurement precision is improved, but loss of substance increases due to high communication costs
Solution Approach 1:
The dataset is divided into two subsets: a first subset of frequently accessed data items encoded using unary encoding, and a second subset of less frequently accessed items encoded using hash-based encoding. This segmentation allows the system to apply different encoding strategies to different portions of the data, achieving high accuracy for important items while reducing overall communication costs.
Solution Approach 2:
Different encoding techniques are applied to different portions of the data based on their access frequency and importance. The first subset (top-m items) receives unary encoding for high accuracy, while the second subset receives hash-based encoding for cost efficiency. This local differentiation optimizes the balance between precision and communication overhead.
2Loss of substance
If hash-based encoding is used for all data items, then loss of substance is reduced, but measurement precision deteriorates due to hash collisions
Solution Approach 1:
The data is segmented into two groups based on access frequency. Frequently accessed items in the first subset use unary encoding to avoid hash collisions and maintain high accuracy, while less frequently accessed items in the second subset use hash-based encoding to minimize communication overhead.
Solution Approach 2:
The encoding quality is adapted locally based on data characteristics. Critical frequently accessed items receive the more accurate unary encoding, while non-critical items use the more efficient hash-based encoding, optimizing the overall system performance.
3Measurement precision
If data is transmitted without anonymization, then measurement precision is improved, but object-affected harmful factors increase due to privacy and security risks
Solution Approach 1:
Local differential privacy mechanisms are applied to each bit of the encoded data before transmission. This preliminary anonymization action protects user privacy and security while allowing the recipient system to perform statistical analysis on the anonymized data, maintaining utility despite the privacy protections.
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for encoding data on client devices. One of the methods includes obtaining (302) dataset for transmission; dividing (304) the dataset into two subsets according to one or more selection criteria, wherein data items that satisfy the one or more criteria are added to a first subset and the data items that do not satisfy the one or more criteria are added to a second subset, wherein the first subset is smaller than the second subset; encoding (306) the data items of the first subset according to a first encoding technique and encoding the data items of the second subset according to a second encoding technique; applying (308) a local differential privacy, LDP, mechanism to data elements of the first and second subsets to generate an LDP message; and transmitting (310) the LDP message to an external recipient.