Privacy Protection Module Distorting Real-Time Data Against Inference Attacks
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Solution Overview
Problem
Users rely heavily on service providers for data privacy protection, which is inadequate due to data breaches and inference attacks, leading to concerns over private data exposure, and there is a need for technologies that provide users with more control over their data while maintaining service quality.
Innovation Solution
A system and method that involves receiving private data, determining the inference privacy risk level, and distorting real-time data based on this risk before transmission to protect sensitive information without compromising service quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time data is transmitted to service providers for personalized services, then service quality is improved, but private data may be inferred from the transmitted data
Solution Approach 1:
The patent introduces an intermediary processing step between data collection and service provision. A privacy protection module acts as a mediator that transforms raw real-time data into anonymized or perturbed data before transmission to service providers, preventing direct inference of private information while maintaining service functionality
Solution Approach 2:
The patent converts the potentially harmful transmitted data into beneficial anonymized data through deliberate distortion techniques. By adding controlled noise or applying transformations, the system turns raw data that could be exploited for inference attacks into protected data that still preserves utility for personalized services
2Object-affected harmful factors
If data distortion is applied to protect private data, then inference attack risk is reduced, but service quality may be compromised
Solution Approach 1:
The patent dynamically adjusts distortion parameters based on the sensitivity of the data and the requirements of the service. By changing parameters such as noise level, transformation intensity, or anonymization degree, the system optimizes the balance between privacy protection and service quality for different data types and service contexts
Solution Approach 2:
The patent applies partial distortion only to specific data fields or attributes that are most susceptible to inference attacks, rather than uniformly distorting all data. This selective approach protects sensitive information while preserving the quality of data needed for service delivery
3Ease of operation
If users rely on service providers for data protection, then ease of operation is improved, but reliability of data privacy protection is reduced
Solution Approach 1:
The patent implements self-service privacy protection by embedding privacy protection functionality directly in the user's device. The local privacy protection module autonomously processes data before transmission without requiring user intervention or trust in external providers, enabling users to protect their own data while maintaining ease of service access
Data Source
AI summary
One embodiment provides a method comprising receiving general private data identifying at least one type of privacy-sensitive data to protect, collecting at least one type of real-time data, and determining an inference privacy risk level associated with transmitting the at least one type of real-time data to a second device. The inference privacy risk level indicates a degree of risk of inferring the general private data from transmitting the at least one type of real-time data. The method further comprises distorting at least a portion of the at least one type of real-time data based on the inference privacy risk level before transmitting the at least one type of real-time data to the second device.