Privacy-Preserving Data Transformation via Adversarial Neural Networks
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Solution Overview
Problem
Existing data anonymization methods struggle to effectively protect user privacy while preserving the analytical usefulness of aggregated data, especially when dealing with high-dimensional and continuous data, and often require known data and observation models, which limits their applicability.
Innovation Solution
A system and method that uses a parametric function, such as a neural network, to transform data and achieve a theoretically optimal tradeoff between privacy and utility by minimizing distortion and leakage of sensitive information, employing an adversarial neural network to estimate mutual information and optimize the transformation process, even when data models are unknown.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data anonymization methods are used to protect user privacy, then privacy is improved, but analytical usefulness of the data deteriorates
Solution Approach 1:
The patent transforms the data release mechanism by changing from deterministic to randomized parameter selection. The mechanism randomly selects transformation parameters from a distribution, which prevents adversaries from inferring sensitive information while preserving statistical properties needed for analytical queries. This randomization achieves optimal privacy-utility tradeoff by making the transformation adaptive rather than fixed.
Solution Approach 2:
The patent introduces an auxiliary mechanism that acts as an intermediary between the sensitive data and the released data. This auxiliary component estimates mutual information and guides the transformation process, serving as a mediator that balances privacy protection with utility preservation without directly exposing sensitive information.
2Reliability
If model-based approaches are used for data anonymization, then privacy protection is achieved, but device complexity and difficulty of implementation increase
Solution Approach 1:
The patent makes the system self-adaptive by using the data itself to learn the transformation mechanism rather than requiring pre-specified models. The mechanism learns optimal transformations directly from the data distribution, eliminating the need for complex manual model design and making the system automatically adaptable to different data types and dimensions.
Solution Approach 2:
The patent replaces traditional mechanical model-based approaches with a learning-based system. Instead of manually designing transformation models based on assumptions about data distributions, the system learns the appropriate transformations directly from data, substituting complex model design with automated learning processes that are more adaptable and easier to implement.
3Ease of operation
If deterministic data release mechanisms are used, then implementation is simple, but privacy protection effectiveness deteriorates
Solution Approach 1:
The patent transitions from static deterministic transformation to dynamic randomized transformation. The transformation mechanism dynamically selects parameters based on random sampling from learned distributions, making the transformation adaptive and privacy-preserving while maintaining implementation simplicity through automated parameter selection rather than complex manual configuration.
Data Source
AI summary
A communication system including a receiver to receive training data. An input interface to receive input data coupled to a hardware processor and a memory. The hardware processor is configured to initialize the privacy module using the training data. Generate a trained privacy module, by iteratively optimizing an objective function. Wherein for each iteration the objective function is computed by a combination of a distortion of the useful attributes in the transformed data and of a mutual information between the sensitive attributes and the transformed data. Such that the mutual information is estimated by the auxiliary module that maximizes a conditional likelihood of the sensitive attributes given the transformed data. Receive the input data via the input interface. Apply the trained privacy module on the input data to produce an application specific transformed data. A transmitter to transmit the application specific transformed data over a communication channel.