Homomorphic Encryption Secure Compute Engine for Cloud Data Confidentiality
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Solution Overview
Problem
Conventional approaches to cloud computing and collaborative data analysis face challenges in maintaining data confidentiality, as encrypted data needs to be decrypted for analysis, and there is a lack of a unifying architecture to ensure end-to-end security and privacy.
Innovation Solution
A system and method using a secure compute engine that applies transformations to data to make it compatible with cloud platforms, employing cryptographic techniques like Shamir's secret sharing and homomorphic encryption, allowing secure operations to be performed without revealing the data to cloud providers, and enabling aggregation of data from multiple parties while maintaining confidentiality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is encrypted for security, then data confidentiality is improved, but data cannot be analyzed because decryption is required
Solution Approach 1:
The patent transforms data into a special encrypted format that preserves computational properties. By changing the parameter of data representation to homomorphically encrypted form, the system enables mathematical operations to be performed directly on encrypted data, allowing both confidentiality and analysis capability to coexist
Solution Approach 2:
The patent introduces a homomorphic encryption system as an intermediary between the data and the analysis process. This intermediary allows computations to be performed on encrypted data without decryption, acting as a bridge that maintains security while enabling analytical operations through specialized cryptographic protocols
2Productivity
If data is shared for collaborative analysis, then analytical capability is improved, but data privacy is compromised because parties must reveal their data
Solution Approach 1:
The patent transforms each party's data into homomorphically encrypted form before sharing, changing the parameter of data representation. This allows collaborative computations to be performed on the encrypted data from multiple parties without any party needing to reveal their raw data, maintaining privacy while enabling joint analysis
Solution Approach 2:
The homomorphic encryption system serves as an intermediary that enables collaborative analysis without direct data exposure. Each party can contribute encrypted data to a shared computational process, and the encryption protocol mediates the interaction to produce joint results without any party seeing the other's raw data
3Reliability
If conventional encryption is used for cloud computing, then security is improved, but the solution is incomplete because there is no unifying architecture for end-to-end security
Solution Approach 1:
The patent implements a universal homomorphic encryption framework that can handle multiple types of computations (arithmetic operations, statistical analysis, machine learning) on encrypted data through a single unified cryptographic protocol. This multi-functional approach provides end-to-end security across diverse analytical workloads without requiring separate encryption schemes for each operation type
Data Source
AI summary
Disclosed herein are systems and method for performing secure computing while maintaining data confidentiality. In one exemplary aspect, a method receives, via an application, both data and a request to perform a secure operation on the data, wherein the secure operation is to be performed using a secure compute engine on a cloud platform such that the data is not viewable to a provider of the cloud platform. The method applies transformations to the data so that the data is not viewable to the provider. The method transmits the transformed data to the secure compute engine on the cloud platform to perform the secure operation on the transformed data, receives a result of the secure operation from the secure compute engine, and transmits the result to the application.