Privacy Score Evaluator for Shared Cloud Data Containers
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Solution Overview
Problem
In a shared resource environment, data stewards face challenges in ensuring privacy preservation and detecting potential data leakages, as existing technologies lack effective mechanisms to assess and maintain privacy levels across distributed computing resources, particularly in cloud services where data is shared and anonymized.
Innovation Solution
A system and method utilizing an Auditing and Privacy Verification Evaluator (Evaluator) that assesses privacy preservation by computing a privacy score based on auxiliary information and specific characteristics of anonymization services, identifying inferred entities that violate preferred privacy levels, and populating a data container with entities that compromise privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is shared and anonymized in cloud services, then data accessibility and utility are improved, but privacy preservation and security are compromised
Solution Approach 1:
The patent implements a feedback mechanism where privacy scores are continuously computed and monitored. The Evaluator receives privacy scores from multiple sources, assesses them against thresholds, and triggers notifications when privacy breaches are detected. This closed-loop feedback system enables dynamic adjustment and response to privacy risks while maintaining data sharing operations.
Solution Approach 2:
The patent introduces an intermediary Evaluator component that acts as a mediator between data sharing operations and privacy protection. The Evaluator receives privacy scores, performs confidence level assessments, and generates notifications without directly interfering with the underlying data sharing infrastructure, thus preserving accessibility while enforcing privacy standards.
2Reliability
If privacy assessment mechanisms are implemented, then privacy preservation is improved, but system complexity and computational overhead increase
Solution Approach 1:
The Evaluator is designed as a universal component that handles multiple functions: receiving privacy scores from various sources, performing confidence level assessments, determining breach conditions, and generating notifications. This multi-functional design consolidates complexity into a single versatile module rather than distributing it across multiple specialized components.
Solution Approach 2:
The system implements self-service through automated privacy score computation and assessment. The Evaluator autonomously receives privacy scores, evaluates them against predefined thresholds, and triggers notifications without requiring manual intervention, thereby reducing operational complexity while maintaining robust privacy protection.
3Measurement precision
If continuous privacy monitoring is performed, then privacy breach detection is improved, but processing time and computational resources are consumed
Solution Approach 1:
The patent implements periodic action through event-driven privacy score assessment. Instead of continuous monitoring, the system periodically evaluates privacy scores when new scores are received or when thresholds are crossed. This approach maintains high detection accuracy while minimizing unnecessary computational overhead during stable periods.
Solution Approach 2:
The system dynamically adjusts assessment parameters based on incoming privacy scores. The Evaluator modifies confidence level thresholds and assessment intensity based on the severity and nature of detected anomalies, thereby optimizing processing time while maintaining detection accuracy for critical privacy breaches.
Data Source
AI summary
Embodiments relate to a system, program product, and method for use with a computer platform to support privacy preservation. The platform measures and verifies data privacy provided by a shared resource service provider. An assessment is utilized to support the privacy preservation with respect to a data steward, and associated shared data. It is understood that data associated with a data service has an expected level of privacy. A privacy score directly correlating to a leakage indicator of the service is formed, and an associated data container is populated with inferred entities deemed to at least meet a preferred privacy level. The privacy score effectively certifies the security of the populated data container.