Fuzzy Biometric Query Protocol for Private Sublinear Search
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Solution Overview
Problem
Existing biometric surveillance systems face challenges in maintaining privacy while performing real-time identification, as they often store and process biometric data in plaintext, leading to potential privacy breaches and inefficient search operations that scale linearly with database size.
Innovation Solution
A privacy-preserving fuzzy query system using a fuzzy labeled set intersection (FLPSI) protocol that encrypts biometric data and employs multiple-party computation to allow clients to search a database without revealing query content to the server, while ensuring efficient sublinear search scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If biometric data is stored and processed in plaintext for real-time identification, then search functionality and identification accuracy are improved, but privacy protection deteriorates
Solution Approach 1:
The patent introduces cryptographic intermediaries (encryption schemes, hash functions, and secure multi-party computation protocols) that mediate between the biometric data and the search process. These intermediaries allow the system to perform identification without exposing raw biometric data, thus maintaining privacy while enabling real-time search functionality.
Solution Approach 2:
The patent transforms biometric data from its original plaintext form into encrypted representations or cryptographic hashes. This parameter change in data representation allows the system to perform similarity comparisons and identification operations on transformed data without revealing the original biometric information, thereby protecting privacy while maintaining identification capability.
2Speed
If plaintext biometric data is stored for easy searching, then search speed is improved, but data security and privacy protection worsen
Solution Approach 1:
Cryptographic intermediaries are introduced to enable secure searching. The system uses encrypted data structures and secure computation protocols that allow search operations to be performed on encrypted biometric data without decrypting it first, thus maintaining both security and search functionality.
Solution Approach 2:
The patent changes the parameter of data representation from plaintext to encrypted form or cryptographic hashes. This transformation enables the system to perform efficient similarity searches using cryptographic operations while maintaining data security, as the transformed data can be compared without revealing the original sensitive information.
3Object-affected harmful factors
If traditional private matching protocols are used for privacy-preserving search, then privacy protection is improved, but communication and computation resources increase linearly with database size
Solution Approach 1:
The patent segments the database into multiple partitions or blocks, and applies indexing structures to organize the encrypted data. This segmentation allows the search process to operate on smaller subsets of data rather than the entire database, reducing communication and computation resources while maintaining privacy protection through cryptographic operations on segmented data.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and indexing the encrypted biometric data during database setup. Cryptographic hash indexes and encrypted data structures are built in advance, allowing subsequent search operations to be performed efficiently without linearly scaling resources with database size. This preliminary organization enables sublinear search complexity while maintaining privacy.
Data Source
AI summary
An exemplary system and method are disclosed that provide the private querying of a biometric scan, such as a person's photo, against a private database such that the client issuing the private query learns only the identity of the query if it is in the database while the server performing the processing learns nothing of the biometric scan. The exemplary system and method conduct privacy-preserving searches over fuzzy databases via a fuzzy labeled set intersection (FLPSI) protocol. FLPSI protocol computes an intersection of noisy input sets to assess for closeness/similarity metrics in an efficient and optimized search.