Encrypted K-Mer Abundance Histograms for Private Genome Analysis
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Solution Overview
Problem
Existing k-mer abundance estimation techniques do not address the issue of data privacy, particularly in genome analysis involving sensitive genetic information, which can lead to irreversible damage if leaked.
Innovation Solution
A method and system for computing k-mer abundance histograms on fully homomorphic encrypted genomic data using a privacy-preserving kmerlight algorithm, employing techniques like sketching, hashing, and Fully Homomorphic Encryption (FHE) to maintain privacy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing k-mer abundance estimation techniques are used, then computational efficiency is achieved, but data privacy is compromised
Solution Approach 1:
The patent introduces Fully Homomorphic Encryption (FHE) as an intermediary layer that enables k-mer abundance estimation computations to be performed on encrypted genomic data without decryption. This mediator allows the analysis system to process data in encrypted form, maintaining privacy while achieving computational results, thus resolving the contradiction between data privacy and computational efficiency
Solution Approach 2:
The patent transforms the computational parameters by operating in the encrypted domain rather than plaintext. By changing the state of the data from unencrypted to fully homomorphically encrypted, the system enables privacy-preserving computation. The FHE operations allow arithmetic computations on ciphertexts, maintaining computational efficiency while improving data privacy through parameter transformation
2Measurement precision
If genomic data is decrypted for analysis, then accurate k-mer abundance estimation is achieved, but data security is compromised
Solution Approach 1:
The patent applies preliminary encryption to the genomic data before it enters the k-mer abundance estimation pipeline. By pre-encrypting the data with FHE, the system ensures that subsequent processing operations occur on encrypted data, eliminating the need for decryption during analysis and thus preventing data security risks while maintaining estimation accuracy
Solution Approach 2:
FHE serves as an intermediary that bridges the gap between data security and measurement precision. It allows the system to perform accurate k-mer abundance estimation on encrypted data by enabling arithmetic operations on ciphertexts, thus achieving both data security and measurement precision simultaneously without requiring data decryption
3Reliability
If traditional k-mer analysis methods are used, then processing speed is maintained, but privacy protection is lost
Solution Approach 1:
The patent changes the computational parameter from plaintext processing to encrypted domain processing using FHE. This parameter change enables privacy protection while maintaining processing capability, as FHE operations can perform arithmetic computations on encrypted data. The system accepts some computational overhead in exchange for achieving privacy protection, resolving the contradiction between privacy and processing time
Data Source
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AI summary
With the growing use of large-scale datasets containing participants' genomics and clinical data for research and studies purposes, it is important to ensure the privacy of the participants by generating a secure representation of genome data observations. Embodiments herein provide a system and method to perform DNA analysis on an encrypted genome without the need for decryption and to ensure users' genome privacy. Herein, a stream of input genome is divided into chunks called sketches, where each k-mer in the sketch is hashed to get a sampling level and a counter value for the k-mer in the sketch. A privacy-enhancing technique is designed over the representations using Fully Homomorphic Encryption (FHE). Genomic data is analyzed with identified efficient algorithms for real-world deployment in the encrypted domain.