Homomorphic Encryption for Secure Distributed Task Processing
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Solution Overview
Problem
In conventional distributed processing systems for big data analysis, companies face challenges in securely utilizing computing resources from other companies without disclosing sensitive analysis information, especially when trustworthiness is a concern.
Innovation Solution
An information processing device and method that encrypts and divides tasks, allowing secure execution on acceptor servers without revealing analysis information, using fully homomorphic encryption to maintain privacy and combine results securely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If companies use distributed processing systems to utilize computing resources from other companies, then resource utilization efficiency is improved, but information security deteriorates because analysis information must be disclosed to acceptors
Solution Approach 1:
The task is divided into multiple sub-tasks that are distributed to different acceptor servers for parallel execution. Each sub-task processes a portion of the data independently, and the results are aggregated to form the final output. This segmentation allows resource distribution while maintaining information security through homomorphic encryption.
Solution Approach 2:
Homomorphic encryption acts as an intermediary mechanism that enables computation on encrypted data without decryption. The user's data remains encrypted throughout the processing pipeline, allowing acceptor servers to perform calculations on ciphertexts while the user retains control over the plaintext through the decryption key.
2Productivity
If companies disclose analysis information to acceptors for processing, then computing resource utilization is improved, but trustworthiness deteriorates
Solution Approach 1:
Homomorphic encryption serves as a trustless intermediary that eliminates the need for mutual trust between user and acceptor. The encryption scheme mathematically guarantees that the acceptor cannot access the plaintext data, transforming a trust-based relationship into a mathematically secure one.
Solution Approach 2:
The data is transformed from plaintext to ciphertext through encryption, changing its state to a form that can be processed computationally while preserving the ability to retrieve the original information only through authorized decryption. This parameter change enables secure outsourcing of computation.
3Loss of information
If data is encrypted and divided into sub-tasks for distributed execution, then information security is improved, but device complexity increases
Solution Approach 1:
The complex encryption and processing system is segmented into distinct functional modules: encryption module, task division module, distribution module, execution module, and result aggregation module. Each module handles a specific aspect of the process, making the overall complex system manageable through modular design.
Data Source
AI summary
An information processing device includes: a storage that stores execution reservation information transmitted from a user server, wherein the execution reservation information represents an execution reservation of acceptor servers that execute first tasks that have been encrypted and divided; and a processor that: receives a second task from the user server, encrypts and divides the second task to generate the first tasks, requests the acceptor servers to execute the generated first tasks based on the execution reservation information stored in the storage, receives first execution results from the acceptor servers, decrypts and combines the first execution results to generate a second execution result, and transmits the generated second execution result to the user server.