Single Feature Vector Encryption for Image Privacy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing feature extraction methods produce multiple feature vectors for images, which are not amenable to secure encryption, making them susceptible to attacks and compromising image privacy during comparison.
Innovation Solution
Generating a single feature vector from multiple feature vectors extracted from an image, which is then encrypted, resulting in higher entropy and improved security for comparison purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple feature vectors are extracted from an image, then feature representation completeness is improved, but security against attacks deteriorates
Solution Approach 1:
The patent combines multiple feature vectors extracted from an image into a single aggregated feature vector. This merging process maintains the comprehensive feature representation while creating a unified structure that can be securely encrypted and compared, thereby resolving the contradiction between feature completeness and security.
2Reliability
If feature vectors are encrypted for security, then image privacy is protected, but comparison capability deteriorates
Solution Approach 1:
The patent applies encryption transformation to the aggregated feature vector, changing its parameter representation while preserving the essential comparison capability. The encryption scheme allows for secure comparison of encrypted feature vectors without requiring decryption, thus maintaining both privacy protection and comparison functionality.
3Reliability
If multiple feature vectors are individually encrypted, then security entropy is increased, but operational complexity deteriorates
Solution Approach 1:
The patent aggregates multiple feature vectors into a single feature vector before encryption, which simplifies the encryption operation to a single process rather than multiple individual encryptions. This approach maintains security through the entropy of the aggregated data while reducing operational complexity by eliminating the need to manage multiple encryption operations.
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A method may include extracting feature-data from an image which includes location data that indicates multiple locations within the image and multiple feature vectors that each correspond to one of the multiple locations. Each feature vector may represent a feature of an object in the image. The method may include determining a feature-data plot based on relative positions of the multiple locations and selecting a grid for the feature-data plot. The method may include generating a first representative feature vector for a first cell of the grid based on a first feature vector set and generating a second representative feature vector for a second cell of the grid based on a second feature vector set. The method may include generating a single feature vector that represents the object in the image based on the first representative feature vector and the second representative feature vector and encrypting the single feature vector.