Privacy-Preserving Data Quality Detection via Hashed Vector Dot Products
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Solution Overview
Problem
Existing data quality systems face challenges in preserving privacy while detecting inconsistent data in databases, particularly when collaborating with third parties, as they require sharing sensitive information and proprietary rules, which can reveal private data and rules.
Innovation Solution
A privacy-preserving system and method using hashed components to transform data quality rules and records into vectors for secure dot product computation, ensuring that only the data owner receives the results of inconsistency detection, without revealing the rules or data content to the other party.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cryptographic techniques are used to preserve privacy of data and rules, then privacy is protected, but computational cost increases and system complexity increases
Solution Approach 1:
The patent replaces complex cryptographic mechanisms with a hash-based vector dot product system. Instead of using encryption/decryption protocols, the system transforms data records and rules into hash vectors and computes privacy-preserving dot products to detect violations, significantly reducing computational overhead while maintaining privacy guarantees
Solution Approach 2:
The patent changes the fundamental parameters of privacy-preserving computation from cryptographic operations to hash-based mathematical operations. By transforming the problem into a vector space with hash functions, the system achieves privacy protection through mathematical properties of hash collisions rather than through cryptographic secrecy, reducing both computational cost and system complexity
2Reliability
If cryptographic techniques are used to preserve privacy, then privacy is protected, but processing speed decreases
Solution Approach 1:
The patent substitutes cryptographic processing with hash-based vector operations. The transformation of records and rules into hash vectors followed by dot product computation is significantly faster than cryptographic operations, enabling high-speed processing of large datasets while preserving privacy through the one-way nature of hash functions
Solution Approach 2:
The patent uses disposable hash vectors that are computed once and then discarded after the dot product computation. These hash-based representations serve as temporary, inexpensive proxies for the actual data, enabling rapid privacy-preserving processing without the overhead of maintaining cryptographic key management systems
3Manufacturing precision
If data quality rules are applied to detect inconsistent records, then data quality improves, but privacy of data and rules is compromised
Solution Approach 1:
The patent introduces hash vectors as an intermediary between the raw data records and the data quality rules. Instead of directly comparing sensitive data with sensitive rules, the system computes dot products of their hash representations, allowing quality detection while keeping both data and rules private throughout the process
Solution Approach 2:
The patent creates hash-based copies of the actual data records and rules. These hash vectors serve as surrogate representations that retain the essential structural properties needed for quality detection while completely obscuring the underlying sensitive information, enabling privacy-preserving analysis
Data Source
AI summary
A computer-implemented method for detecting a set of inconsistent data records in a database including multiple records, comprises selecting a data quality rule representing a functional dependency for the database, transforming the data quality rule into at least one rule vector with hashed components, selecting a set of attributes of the database, transforming at least one record of the database selected on the basis of the selected attributes into a record vector with hashed components, computing a dot product of the rule and record vectors to generate a measure representing violation of the data quality rule by the record.