Guessing Anonymization Noise Optimization
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Solution Overview
Problem
Current data anonymization techniques face challenges in balancing privacy and data distortion, with K-anonymity methods complicating data analysis and randomization methods failing to ensure privacy levels, making it difficult to share structured data while protecting individual privacy.
Innovation Solution
The approach defines privacy using the guessing anonymity concept, formulating optimization problems to minimize data distortion while ensuring a minimum privacy level through noise perturbation, allowing for controlled anonymization of structured data to be shared with third parties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If K-anonymity methods are used to protect privacy, then privacy level is improved, but data distortion increases and data analysis becomes complicated
Solution Approach 1:
The patent applies parameter changes by transforming discrete categorical data into continuous numerical data through encoding schemes (e.g., one-hot encoding, integer encoding). This transformation allows the data to be treated as continuous variables, enabling the use of differential privacy techniques that add controlled noise to numerical values while maintaining data utility and simplifying analysis workflows.
2Loss of information
If randomization methods are used to preserve data utility, then data distortion is reduced, but privacy level cannot be assured
Solution Approach 1:
The patent implements feedback through iterative optimization processes where the system evaluates the trade-off between privacy protection and data utility, adjusts noise parameters accordingly, and re-evaluates until optimal balance is achieved. The differential privacy mechanism provides feedback on privacy loss metrics to guide the noise addition process, ensuring both privacy guarantees and data utility are maintained.
3Reliability
If noise is added to sanitize data, then privacy is protected, but control over the balance between privacy and distortion is limited
Solution Approach 1:
The patent applies dynamics by implementing adaptive noise mechanisms where the amount and type of noise added to different data elements are dynamically adjusted based on sensitivity analysis, data distribution characteristics, and desired privacy parameters. This allows the system to automatically optimize the balance between privacy protection and data utility without requiring manual tuning for each dataset.
Data Source
AI summary
Privacy is defined in the context of a guessing game based on the so-called guessing inequality. The privacy of a sanitized record, i.e., guessing anonymity, is defined by the number of guesses an attacker needs to correctly guess an original record used to generate a sanitized record. Using this definition, optimization problems are formulated that optimize a second anonymization parameter (privacy or data distortion) given constraints on a first anonymization parameter (data distortion or privacy, respectively). Optimization is performed across a spectrum of possible values for at least one noise parameter within a noise model. Noise is then generated based on the noise parameter value(s) and applied to the data, which may comprise real and/or categorical data. Prior to anonymization, the data may have identifiers suppressed, whereas outlier data values in the noise perturbed data may be likewise modified to further ensure privacy.