Dynamic Data Dissemination Using Bloom Filters for Privacy Compliance
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Solution Overview
Problem
Current data dissemination methods are costly and time-consuming due to static preprocessing for right to forget and consent revocation requests, which require deep purging and de-aliasing of data subject information, and fail to maintain data privacy effectively.
Innovation Solution
Implementing dynamic data dissemination using Bloom filters for real-time processing and anonymization, which allows for dynamic row filtering and operator insertion directly into the data consumption path, optimizing space and time by using Bloom filters for data subject mapping and maintaining separate lists for consent management and right to forget requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static preprocessing is used for right to forget and consent revocation requests, then data privacy protection is achieved, but processing cost and time increase significantly
Solution Approach 1:
The patent pre-computes and stores Bloom filter representations of data subject identifiers in the data asset catalog before actual data dissemination requests. This preliminary action allows the system to quickly check whether data subject information exists in data assets without performing expensive deep purging and de-aliasing operations at request time, thus resolving the contradiction between privacy protection reliability and processing efficiency
Solution Approach 2:
The patent introduces Bloom filters as an intermediary data structure between the data asset catalog and the actual data storage. These Bloom filters serve as a lightweight filtering layer that can quickly determine the presence or absence of data subject identifiers without requiring access to the actual personal data, enabling efficient privacy compliance checks while maintaining processing speed
2Reliability
If deep purging and de-aliasing of data subject information is performed, then consent revocation and right to forget requests are fulfilled, but storage and computational resources are consumed
Solution Approach 1:
The patent extracts only the essential identifying information (data subject identifiers) from the data assets and creates Bloom filter representations of these identifiers. This extraction approach allows the system to track and manage consent revocation and right to forget requests without storing or processing the actual personal data, significantly reducing storage resource consumption while maintaining accurate consent management
Solution Approach 2:
The patent creates simplified copies of data subject identifier information in the form of Bloom filters, stored in the data asset catalog. These copies contain only the necessary information to determine data subject presence without duplicating the actual personal data, enabling efficient consent management with minimal storage overhead
3Reliability
If real-time data processing is implemented for data dissemination, then data privacy compliance is improved, but processing time and system complexity increase
Solution Approach 1:
The system pre-computes Bloom filter representations of data subject identifiers and stores them in the data asset catalog before actual data dissemination requests arrive. This preliminary preparation enables real-time privacy compliance checks without adding complexity to the data processing pipeline, as the filtering logic is already in place and ready to be applied
Solution Approach 2:
The patent replaces complex mechanical data processing operations (deep purging, de-aliasing, and scanning of actual personal data) with a lightweight Bloom filter-based filtering mechanism. This substitution dramatically simplifies the system architecture while enabling real-time privacy compliance, as Bloom filters can be queried in constant time without requiring access to or processing of actual personal data
Data Source
AI summary
Dynamic data dissemination is provided. A resolved data subject identifier corresponding to a data subject is selected from a set of resolved data subject identifiers existing in rows of a data asset. In response to determining that the resolved data subject identifier does not correspond to a right to forget list, it is determined that the resolved data subject identifier corresponds to a data subject request list. The rows are transformed to anonymize existing pseudo and personal identifiers in cells of the rows that are tied to columns associated with data classes for which specific consent dimensions have been indicated as revoked by the data subject.