Homomorphic Encryption Data Classification for Confidentiality
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Solution Overview
Problem
Conventional homomorphic encryption methods incur significant computation and data size increases due to encrypting all data, limiting efficient processing, especially when only some data requires encryption for analysis, and struggle with operations on entire encrypted datasets.
Innovation Solution
An apparatus and method that classify data into encryption target and non-encryption target data based on sensitivity and required operations, using homomorphic encryption only for sensitive or operation-specific data, allowing both encrypted and unencrypted data to be used for analysis while maintaining confidentiality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all data is encrypted using homomorphic encryption for privacy protection, then data confidentiality is improved, but data size increases by several thousand times and computation cost increases
Solution Approach 1:
The patent segments the original data set into multiple categories including sensitive information requiring encryption, data requiring specific operations, and other data. This segmentation allows selective application of homomorphic encryption only to necessary portions rather than the entire dataset, thereby reducing overall data size while maintaining required confidentiality.
Solution Approach 2:
The patent applies different quality treatments to different parts of the data based on their sensitivity and operational requirements. Sensitive data receives full homomorphic encryption protection, while other data receives appropriate processing. This local quality approach optimizes the balance between confidentiality and data size by applying encryption strength where specifically needed rather than uniformly across all data.
2Reliability
If all data is encrypted using homomorphic encryption, then data confidentiality is improved, but computation cost increases
Solution Approach 1:
The patent segments the data set to identify only sensitive information and data requiring specific operations for encryption. This segmentation reduces the volume of data subjected to computationally intensive homomorphic encryption operations, thereby lowering overall computation cost while maintaining confidentiality where required.
Solution Approach 2:
The patent applies partial action by encrypting only the necessary portion of data (sensitive information and data requiring specific operations) rather than the entire dataset. This partial application of homomorphic encryption significantly reduces computation cost while providing adequate protection for the critical data portions.
3Productivity
If multiple data are encrypted into a single ciphertext for efficiency, then computation efficiency is improved, but processing operations on entire encrypted data (SIMD) becomes limited
Solution Approach 1:
The patent segments encrypted and unencrypted data into separate categories, allowing different processing approaches for each. This segmentation enables versatile operation processing on unencrypted data while maintaining efficient batch processing capabilities for encrypted data, thus balancing computation efficiency with operational versatility.
Solution Approach 2:
The patent dynamically adjusts the processing approach based on data type and operational requirements. For encrypted data requiring specific operations, it applies appropriate homomorphic operations; for unencrypted data, it applies standard processing. This dynamic approach maintains computational efficiency while preserving adaptability across different operation types.
Data Source
AI summary
An apparatus for encryption according to an embodiment of the present disclosure comprises a classifier configured to classify each data included in an original data set into one of encryption target data and non-encryption target data on the basis of at least one of determination on whether or not an operation to be applied to each data included in the original data set for an analysis of the original data set is a preset operation and determination on whether or not each data included in the original data set is sensitive information; and an encryptor configured to encrypt the encryption target data among the data included in the original data set using a homomorphic encryption algorithm.