Cascaded Decryption for Secure Homomorphic Metric Aggregation
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Solution Overview
Problem
Existing benchmarking systems face challenges in preserving the confidentiality of proprietary computing performance metrics when aggregating data from multiple sources, as they rely on a single trusted third party that can unintentionally or intentionally expose sensitive information.
Innovation Solution
Implementing a threshold homomorphic encryption scheme with cascaded decryption, where multiple parties contribute public encryption keys to encrypt and decrypt metrics, ensuring that no single party can access the underlying data, and using bitwise operations and bootstrapping to optimize computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single trusted third party is used to aggregate benchmarking metrics, then the aggregation process is simple and efficient, but the confidentiality of proprietary metrics is compromised
Solution Approach 1:
The decryption key is segmented into multiple shares distributed among different parties (benchmarking service and independent decryption service). No single party possesses the complete key, preventing unauthorized decryption while enabling collaborative decryption when needed. This resolves the contradiction by maintaining aggregation efficiency through automated multi-party computation while eliminating data exposure risk through key segmentation.
Solution Approach 2:
An independent decryption service acts as an intermediary between the benchmarking service and metric providers. This intermediary holds a key share and participates in the decryption process, ensuring that no single entity (including the benchmarking service) can unilaterally access confidential metrics. The intermediary enables secure aggregation by mediating the decryption process with proper safeguards.
2Object-affected harmful factors
If multiple parties contribute to decryption keys, then data confidentiality is enhanced, but the decryption process becomes more complex
Solution Approach 1:
The system performs self-service through automated threshold decryption protocols where the benchmarking service and independent decryption service automatically coordinate to decrypt metrics without requiring manual intervention. The complexity of multi-party coordination is handled by predefined cryptographic protocols, making the system appear simple to users while maintaining strong confidentiality through automated key management.
3Object-affected harmful factors
If encrypted metrics are used, then data security is improved, but the ability to compute benchmarks from encrypted data is limited
Solution Approach 1:
The system changes the parameter of data representation from plaintext to encrypted form, and simultaneously changes the computation method to support encrypted operations. By adopting homomorphic encryption or secure multi-party computation protocols, the system enables benchmark calculations to be performed directly on encrypted metrics, maintaining both security and computational capability.
Solution Approach 2:
The independent decryption service serves as an intermediary that facilitates computations on encrypted data by coordinating with the benchmarking service. This intermediary enables the system to perform benchmark calculations on encrypted metrics without requiring plaintext exposure, maintaining ease of operation through automated cryptographic protocols while improving metrics security.
Data Source
AI summary
A data aggregation service is engineered to generate aggregated data values based on encrypted data received from a variety of providers, without being exposed to the underlying plaintext data. A homomorphic encryption scheme is used in a threshold cryptography scenario that allows aggregation of the encrypted data without requiring decryption. An independent decryption service can partially decrypt the aggregated result, which can ultimately be decrypted to plaintext for use by the provider. Bitwise operations can be defined to support aggregation with error tolerance, and the operations can be constrained to a smaller bit size to reduce circuit complexity.