Privacy-Enhancing Transformation Guidance with Risk-Utility Simulation

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Solution Overview

Problem

Existing technologies lack automated decision support systems for recommending privacy-enhancing transformations in datasets, failing to balance privacy risk with data utility effectively.

Innovation Solution

A system and method incorporating a recommendation engine and simulation engine to identify and apply optimal transformations, balancing privacy risk and utility through a case-based approach, utilizing a recommendation library and simulation to generate recommendations and explanations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual transformation selection is used to reduce privacy risk, then privacy protection can be achieved, but the process requires high expertise and significant time investment from data analysts

Engineering Contradiction:

Improveprivacy protectionVSAvoidoperational complexity

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system enables automated self-service through the recommendation engine that autonomously analyzes datasets, evaluates transformation options, and generates privacy-enhancing transformation recommendations without requiring manual expert intervention. The simulation engine then automatically validates these recommendations, allowing the system to serve itself rather than relying on external expert analysis.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of expert analysis with an automated computational system. The recommendation engine uses algorithms to substitute human expert judgment, while the simulation engine replaces manual validation processes, collectively replacing the mechanical workflow of expert data analysts with an automated computational mechanism.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If multiple transformation techniques are applied to reduce privacy risk, then privacy protection improves, but the cognitive burden and time required for selection and application increases

Engineering Contradiction:

Improveprivacy protectionVSAvoidtransformation selection time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The recommendation engine performs preliminary analysis of the dataset to identify suitable transformation techniques before the actual transformation is applied. By pre-evaluating multiple transformation options and their potential impact on privacy risk, the system prepares recommendations in advance, saving time during the actual transformation implementation phase.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The simulation engine provides feedback by measuring the privacy risk and utility of recommended transformations before they are applied to the full dataset. This feedback mechanism allows the recommendation engine to refine and optimize transformation selections, ensuring that the chosen transformations effectively reduce privacy risk while maintaining data utility, thereby reducing iterative trial-and-error time.

Inventive Principle:

Principle #23Feedback

3Productivity

If automated transformation recommendations are implemented, then the process time and expertise requirement are reduced, but the system complexity increases

Engineering Contradiction:

Improvetransformation processing speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system is segmented into distinct functional modules: the recommendation engine that generates transformation suggestions, the simulation engine that validates these suggestions, and the explanation generator that provides rationale. This segmentation allows each component to specialize in a specific task, improving overall productivity while managing complexity through modular design that can be developed and maintained independently.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The simulation engine acts as an intermediary between the recommendation engine and the final transformation application. It mediates by validating recommendations and providing feedback, which reduces the complexity burden on the recommendation engine while maintaining high productivity through automated processing. This intermediary layer simplifies the overall system architecture by creating clear interfaces between components.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250342262A1System and method for guiding privacy-enhancing transformations

Publication Date: 2025.11.06 TRUATA LTD

AI summary

A system and method for guiding privacy-enhancing transformations are described. The system and method include a recommendation engine configured to identify sets of transformations to mitigate a privacy risk below a user specified threshold specified in-terms of privacy-risk score for a given input dataset while keeping the utility of the dataset above the user-specified utility threshold specified in-terms of utility score. A simulation engine configured to simulate the identified set of transformations from the recommendation engine on the dataset to determine the optimal application of the plurality of transformations for maximizing the utility of the dataset, and output device to provide the optimized dataset with the privacy risk score and utility score.