Hyperdimensional Computing Privacy via Hypervector Quantization
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Solution Overview
Problem
Current machine learning models face challenges in privacy preservation, especially in edge devices with limited computation capacity, where cloud-hosted inference is necessary but vulnerable to data exposure and untrusted hosts, and hyperdimensional computing lacks privacy due to reversible operations.
Innovation Solution
Implement differential privacy techniques through hypervector quantization and pruning to reduce sensitivity, and perform inference quantization to obfuscate information, combined with hardware optimizations for efficient implementation on FPGA platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud-hosted inference is used for edge devices with limited computation capacity, then computation capability is improved, but data privacy and security deteriorate due to vulnerability to data exposure and untrusted hosts
Solution Approach 1:
The patent introduces differential privacy as an intermediary mechanism between the edge device and cloud-hosted inference system. By adding carefully calibrated noise to the inference process, it enables computation capability improvement through cloud hosting while protecting data privacy, as the noise prevents direct reconstruction of sensitive input data from inference outputs
Solution Approach 2:
The patent modifies the inference process by changing parameters such as noise addition levels and sensitivity thresholds. This allows the system to balance between maintaining accurate inference results and protecting data privacy, enabling edge devices to leverage cloud computation without directly exposing sensitive data
2Productivity
If hyperdimensional computing is used for efficient processing, then computation speed is improved, but privacy protection deteriorates due to reversible operations that can be exploited for data reconstruction
Solution Approach 1:
The patent applies preliminary action by adding differential privacy noise before the hyperdimensional computing operations are performed. This preliminary modification of the data ensures that even though the reversible operations can theoretically reconstruct data, the added noise prevents accurate reconstruction, thus protecting privacy while maintaining computation speed
Solution Approach 2:
The patent converts the harmful reversibility property of hyperdimensional computing into a benefit by using differential privacy techniques. The noise added to the system, which might seem to degrade precision, actually protects against data reconstruction attacks while allowing the fast reversible operations to continue functioning efficiently
3Object-affected harmful factors
If differential privacy techniques are applied through hypervector quantization and pruning, then data privacy is improved, but computation accuracy deteriorates due to information loss from quantization
Solution Approach 1:
The patent adjusts parameters such as quantization levels, pruning thresholds, and noise addition magnitudes to optimize the balance between privacy protection and accuracy. By carefully tuning these parameters, the system achieves adequate privacy protection while minimizing the degradation of prediction accuracy
Solution Approach 2:
The patent applies partial quantization and pruning rather than complete aggregation, maintaining some precision in critical dimensions while applying privacy protection in others. This selective approach preserves more accuracy compared to uniform quantization across all dimensions
4Object-affected harmful factors
If inference quantization is performed to obfuscate information, then privacy protection is improved, but computational precision deteriorates
Solution Approach 1:
The patent applies local quality by performing quantization selectively in specific regions or dimensions of the hypervector space where privacy protection is most critical, while maintaining higher precision in dimensions essential for accurate inference. This localized approach balances privacy and precision requirements
Data Source
AI summary
A method of searching for a query sequence of nucleotide characters within a chromosomal or genomic nucleic acid reference sequence can include receiving a query sequence representing nucleotide characters to be searched for within a reference sequence of characters represented by a reference hypervector generated by combining respective base hypervectors for each nucleotide character included in the reference sequence of characters appearing in all sub-strings of characters having a length between a specified lower length and a specified upper length within the reference sequence, combining respective near orthogonal base hypervectors for each of the nucleotide characters included in the query sequence to generate a query hypervector, and generating a dot product of the query hypervector and the reference hypervector to determine a decision score indicating a degree to which the query sequence is included in the reference sequence. Other aspects and embodiments according to the invention are also disclosed herein.