Encryption Node Noise Factor Computation for Homomorphic Throughput
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Solution Overview
Problem
Current homomorphic encryption methods, such as the Paillier cryptosystem, are complex and time-consuming, limiting their ability to handle high data throughput and fluctuating data streams, particularly in cloud computing environments where high-speed and irregular data processing is required.
Innovation Solution
An encryption node with a noise computation engine and an encryption engine computes a noise factor independently of the encryption process, allowing for rapid encryption of messages using the formula c=gm·F mod n2, where g and n are predefined parameters, enabling higher throughput by separating noise factor computation from the encryption operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If homomorphic encryption methods like Paillier cryptosystem are used, then data privacy and integrity are protected, but encryption complexity and execution time increase
Solution Approach 1:
The encryption process is segmented into two independent parts: noise factor computation and encryption operation. The noise factor F is computed separately using the formula F = r^n mod n^2, where r is a random number. This noise factor is then used in the encryption formula c = g^m * F mod n^2. By separating these computations, the system reduces the computational burden during encryption while maintaining security.
Solution Approach 2:
The noise factor computation is performed as a preliminary action that can be done independently and potentially in advance. By pre-computing or separately computing the noise factor F = r^n mod n^2, the system prepares a component that simplifies the main encryption operation c = g^m * F mod n^2, reducing the overall complexity and execution time.
2Reliability
If homomorphic encryption methods are used, then data privacy is maintained, but encryption speed and throughput decrease
Solution Approach 1:
The encryption process is divided into independent computational tasks: calculating the noise factor F = r^n mod n^2 and performing the encryption c = g^m * F mod n^2. This segmentation allows for parallel execution and optimization of each part, improving overall encryption throughput while maintaining data privacy through homomorphic encryption.
Solution Approach 2:
The system changes the approach by introducing a noise factor parameter F that is computed separately and then combined with the encryption result. This parameter transformation allows the encryption operation to be simplified and executed faster, as the complex homomorphic encryption can leverage the pre-computed noise factor to achieve both security and speed.
3Reliability
If existing encryption methods are used, then data security is ensured, but computational efficiency and processing speed are reduced
Solution Approach 1:
The noise factor F is computed as a preliminary action using the formula F = r^n mod n^2, where r is a random number. This preliminary computation of the noise factor allows the main encryption operation c = g^m * F mod n^2 to proceed more efficiently, as the complex homomorphic encryption can utilize the pre-prepared noise factor to enhance both security and processing speed.
Data Source
AI summary
A method and encryption node (300) for providing encryption of a message m according to a selected encryption scheme. A noise computation engine (300a) in the encryption node (300) computes (3:1) a noise factor F as a function of a predefined integer parameter n of the selected encryption scheme and a random number r. When the message m is received (3:3) from a client (302) for encryption, an encryption engine (300b) in the encryption node (300), encrypts (3:4) the message m by computing a cipher text c as e=gm·F mod n2, where g is another predefined integer parameter of the selected encryption scheme. The cipher text c is then delivered (3:5) as an encryption of the message m, e.g. to the client (302) or to a cloud of processing resources (304).