An information processing method supporting overflow detection and large integer operation and its application

Abstract

The invention belongs to the technical field of data encryption, and mainly proposes overflow detection and ciphertext segmentation algorithms to support privacy-preserving multiplication for large integers, adopts the ABE algorithm to realize flexible and safe fine-grained access control for encrypted data multiplication calculation results, and finally expands to support basic operations on data types including rational numbers and floating-point numbers. The system calls the key generation algorithm KeyGen to establish a homomorphic re-encryption system; the authoritative organization calls the system to establish the algorithm to generate public parameters and the master key of ABE, and releases it to the DR; the DP calls the EnkTK encryption algorithm to encrypt the data and upload it to the DSP, and the DSP receives After receiving the encrypted data, cooperate with the CP to process the data. First, overflow detection is performed on the ciphertext. If there is no overflow, the multiplication operation is called directly; otherwise, the ciphertext segmentation algorithm is first called to divide the large integer ciphertext, and then the multiplication operation is called Solve; only the DR that meets the access policy can decrypt CK to obtain the corresponding decryption key ck, and then use ck to decrypt to obtain the plaintext.

Description

technical field [0001] The invention belongs to the technical field of data encryption, and in particular relates to an information processing method and application supporting overflow detection and large integer calculation. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: cloud computing utilizes a large number of resources and powerful computing capabilities in the network to provide cloud users with various services (for example, data storage and massive data analysis), these services are Great convenience for cloud users with limited local resources. However, outsourcing personal data to cloud servers makes cloud users lose complete control over personal data, and semi-trusted cloud servers may leak users' personal privacy data to other unauthorized entities, seriously destroying user privacy and threatening data security. Therefore, cloud users often choose to encrypt data first and then outsource it to...

AI Technical Summary

Problems solved by technology

First, the ciphertext limits the computability of the original data

Secondly, encryption limits the length of the original data and the number of data in the multiplication operation, because the length of the multiplication calculation result is the sum of the lengths of the original multipliers, so the length restriction on the original data in the multiplication is more serious

Third, encryption complicates the access control to the results of data processing and analysis, especially fine-grained access control to the results of operations (e.g., multiplication)

Disadvantages of the existing technology: 1. The original information length is limited, and the ciphertext multiplication calculation of large data and large integers is not supported; 2. It does not support the security fine-grained access control of the ciphertext multiplication calculation results; 3. It does not support the overflow detection of the multiplication operation

[0004] (1) Encrypt data before outsourcing to protect user privacy. Encryption increases the complexity of data calculation and analysis, and introduces higher computing and communication overheads

[0005] (2) Most of the existing work does not support the privacy-preserving multiplication of large integers. The existing technology can realize the multiplication of encrypted data, but it can only support data of limited length, and the data length is inversely proportional to the number of data involved in the calculation. Severely limit the length and amount of data

[0006] (3) There is more than one specific user in the Internet of Things who needs to access data fusion or analysis results. For example, several entities (such as relatives, doctors, and nursing staff) may need to access the patient's disease diagnosis, and the flexibility of multiplication results is not realized. Fine-grained access control

2) Existing research often ignores the access control of data analysis results

The homomorphic encryption algorithm that supports data analysis and the fine-grained access control mechanism often use different cryptosystems, which increases the difficulty of combining algorithms.

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