Secure Multiparty Computation for Privacy-Preserving Data Analytics
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Solution Overview
Problem
Existing techniques for sharing sensitive and private data between parties face challenges such as exposure of data in plaintext, expanding the attack surface, and poor computational performance, making it difficult to securely share data while maintaining confidentiality.
Innovation Solution
Implementing secure multiparty computation protocols with a third-party intermediary using cryptographic techniques like homomorphic encryption and keyed one-way functions, such as HMAC, to ensure data remains confidential and allows computations without revealing plaintext data to the service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is shared in plaintext for analytics, then computational performance is improved, but data confidentiality and security are compromised
Solution Approach 1:
The patent changes the state of data from plaintext to encrypted form, allowing computations to be performed on ciphertext. This parameter change enables the system to maintain data confidentiality while still performing analytics, resolving the contradiction between security and computational performance
Solution Approach 2:
The patent introduces cryptographic intermediaries (encryption schemes, hash functions) that mediate between the need for data sharing and the need for confidentiality. These intermediaries enable computations on encrypted data without exposing plaintext, thus maintaining both security and functionality
2Reliability
If data is encrypted to maintain confidentiality, then data security is improved, but computational performance deteriorates
Solution Approach 1:
The patent extracts only the necessary computational operations to be performed on encrypted data, rather than decrypting the entire dataset. By taking out and performing only the essential analytics on ciphertext, the system maintains security while minimizing the performance overhead associated with encrypted computation
3Adaptability or versatility
If data is shared between parties for collaboration, then analytics capability is improved, but attack surface is expanded
Solution Approach 1:
The patent introduces cryptographic intermediaries that enable collaborative analytics without direct data exposure. The third-party service acts as an intermediary that processes encrypted data from multiple parties, performing joint analytics while preventing any single party or the service itself from accessing plaintext, thus expanding analytics capability without proportionally expanding the attack surface
4Productivity
If plaintext data is exposed to third-party service, then data processing capability is improved, but privacy protection is compromised
Solution Approach 1:
The patent changes the data state parameter from plaintext to encrypted form before transmission to the third-party service. This parameter change enables the service to process data with high capability while the encryption maintains privacy protection, as the service operates on ciphertext that reveals no sensitive information
Data Source
AI summary
Systems, devices, and methods are provided for secure multiparty computation (MPC) protocols. A first computing entity may send a first cryptographically protected data set to a server and a second computing entity may send a second cryptographically protected data set to the server. The server may lack access to plaintext versions of the data sets. The server may compare cryptographically protected data elements from the first and second data sets as part of a secure MPC protocol to determine certain information regarding the data sets, such as determining which data elements are included in both sets, and perform homomorphic computations according to a homomorphic encryption scheme. The server is accordingly able to determine an encrypted result.