Distributed Privacy Vulnerability Identification in Data Streams
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Solution Overview
Problem
Existing privacy solutions are inadequate for handling massive and fast data streams, as they are either too slow, require excessive memory, or are not designed to operate in real-time, making it difficult to identify and address privacy vulnerabilities in a streaming and online fashion while being compliant with regulations like EU Data Privacy Act and HIPAA.
Innovation Solution
A method and system that processes data streams in a distributed computing environment by indexing data values, integrating them into a global index structure, and identifying direct identifiers and quasi-identifiers, applying data transformations to provide privacy protection, which can scale to large datasets and operate in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing privacy algorithms are used to identify vulnerabilities in relational tables, then privacy vulnerabilities can be identified, but the algorithms are too slow and cannot scale to medium datasets
Solution Approach 1:
The patent divides the data stream processing into multiple independent partitions that can be processed concurrently by different computational units. Each partition is handled separately, allowing parallel execution of privacy vulnerability identification algorithms, thus improving processing speed while maintaining identification accuracy.
Solution Approach 2:
The system performs preliminary indexing of data values before privacy vulnerability identification. By pre-processing and organizing data into efficient index structures, the system reduces the computational burden during the actual vulnerability detection phase, enabling faster processing without sacrificing accuracy.
2Measurement precision
If existing privacy algorithms are used, then privacy vulnerabilities can be identified, but a prohibitively large amount of memory is required
Solution Approach 1:
The patent extracts only the essential data elements needed for privacy vulnerability identification into compact index structures. By selecting and indexing only relevant attributes and values rather than storing entire datasets in memory, the system reduces memory consumption while maintaining the ability to accurately identify direct identifiers and quasi-identifiers.
Solution Approach 2:
The system transforms the problem from working with high-dimensional raw data to operating on lower-dimensional index structures. By organizing data in terms of attribute-value pairs and their frequency counts rather than complete records, the memory requirements are dramatically reduced while preserving the capability to detect privacy vulnerabilities.
3Reliability
If existing privacy solutions are used, then some privacy protection can be provided, but they are not designed to handle anonymization of massive and fast datasets in a streaming and online fashion
Solution Approach 1:
The patent implements a dynamic system that continuously processes data streams in real-time, adapting to incoming data as it arrives. The privacy vulnerability identification and anonymization processes operate dynamically on streaming data rather than requiring static batch processing, enabling real-time protection while maintaining effectiveness through continuous analysis of attribute frequency distributions.
Solution Approach 2:
The system maintains continuous operation by processing data streams without interruption. Privacy protection is applied continuously as data flows through the system, with ongoing identification of direct identifiers and quasi-identifiers and immediate application of anonymization techniques, ensuring uninterrupted real-time protection for massive datasets.
Data Source
AI summary
A method, system and computer program product for providing privacy protection to data streams in a distributed computing environment. The method includes concurrently processing, by a plurality of computer machines, data streams of attributes containing data values received by each of the plurality of local computer machines; indexing the data values for each attribute of the plurality of data streams received by each of the plurality of local computer machines; providing the indexed data values to a main computer machine; integrating, by the main computer machine, the local computer machine indexed data values into a global index data structure for the plurality of data streams; and identifying privacy vulnerabilities of the attributes that are direct identifiers and quasi-identifiers based on the global index.