Homomorphic Encryption Secret Shuffle Protocol for Encrypted KPIs
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Solution Overview
Problem
Existing secret shuffle protocols in cloud-based multi-party computation scenarios fail to ensure anonymity of encrypted Key Performance Indicators (KPIs) during cross-company benchmarking, as sorting KPIs can reveal relative performance information, posing a risk of data leakage and compromising confidentiality.
Innovation Solution
Implementing secret shuffle protocols using homomorphic encryption, which randomize the order of encrypted inputs without revealing the original order, ensuring that no observer can map elements in the original sequence to their corresponding elements in the shuffled sequence, thereby maintaining anonymity and confidentiality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If encrypted KPIs are sorted during cross-company benchmarking, then performance measurements can be generated, but anonymity of encrypted KPIs is compromised and relative performance information is revealed
Solution Approach 1:
A trusted shuffle service acts as an intermediary that receives encrypted KPIs from multiple companies, shuffles them using secret shuffle protocols based on homomorphic encryption, and returns the shuffled sequence. This mediator enables performance benchmarking while preserving anonymity, as the shuffle service cannot link original positions to shuffled positions without the secret key.
Solution Approach 2:
The system changes the parameter of encryption from standard encryption to homomorphic encryption, which allows mathematical operations on encrypted data. This enables the KPIs to remain encrypted during shuffling and benchmarking, maintaining anonymity while still allowing performance measurements to be computed and compared.
2Loss of information
If secret shuffle protocols are implemented using homomorphic encryption, then anonymity of encrypted inputs is maintained, but computational complexity increases
Solution Approach 1:
The secret shuffle protocol is segmented into multiple independent steps: key generation, encryption of inputs, shuffling with blinding factors, and decryption. Each step can be independently implemented and optimized. The use of homomorphic encryption is confined to specific operations where it provides the necessary security properties, rather than applying it throughout the entire system.
Solution Approach 2:
The shuffle service acts as a mediator that handles the computationally intensive homomorphic encryption operations, while client systems can use lighter-weight cryptographic operations. This distribution of computational burden reduces the complexity burden on individual systems.
3Loss of information
If encrypted data is shuffled to prevent data leakage, then confidentiality is enhanced, but the ability to perform computations on the data is reduced
Solution Approach 1:
The system changes the encryption parameter to homomorphic encryption, which fundamentally alters the relationship between encryption and computation. This allows mathematical operations (addition, multiplication) to be performed directly on encrypted data, enabling computations to be executed on shuffled KPIs without decryption, thus maintaining both confidentiality and computability.
Solution Approach 2:
Data is encrypted and shuffled in advance before any computations are performed. This preliminary encryption and shuffling ensures that all subsequent operations on the data maintain confidentiality, while the homomorphic properties enable computations to proceed on the encrypted shuffled data without requiring decryption at intermediate steps.
Data Source
AI summary
The present disclosure involves systems, software, and computer implemented methods for a communication-efficient secret shuffle protocol for encrypted data based on homomorphic encryption. A service provider and multiple clients participate in a secret shuffle protocol of randomly shuffling encrypted client-specific secret input values. The protocol includes generation and exchange of random numbers, random permutations and different blinding values. A last protocol step includes using homomorphism, for each client, to perform computations on intermediate encrypted data to homomorphically remove a first blinding value and a second blinding value, to generate a client-specific rerandomized encrypted secret input value. As a result, the client-specific rerandomized encrypted secret input values are generated in an order that is unmapped to an order of receipt, at the service provider, of the encrypted secret input values.