Sum and product computing method for protecting data privacy security of arbitrary user group

Abstract

The invention relates to a sum and product computing method for protecting data privacy security of an arbitrary user group. The computing method comprises the following steps of system initialization, user private key generation, secret key generation, sum encryption, product encryption, sum decryption, and product decryption. To be specific, according to the system initialization, a security parameter is specified; and a corresponding integer group and a system public key are generated by the security parameter. According to the user private key generation, participants in the user group compute private keys independently and respectively, so that the product of the private keys is equal to one after modulus operation. According to the secret key generation, a sum secret key and a product secret key for encryption data are generated for the participants based on combination of the system public key and the user private keys. According to the sum encryption, during the sum computing, the participants utilize the obtained secret keys to carry out encryption on the sum computing and send obtained sum ciphertexts to other participants in the user group. According to the product encryption, during product computing, the participants utilize the obtained secret keys to carry out encryption on the product computing and send obtained product ciphertexts to other participants in the user group. According to the sum decryption, the participants in the user group combine the received ciphertexts to obtain a final sum. And according to the product decryption, the participants in the user group combine received ciphertexts to obtain a final product.

Description

technical field [0001] The invention relates to the technical field of secure multi-party computing, in particular to a sum and product computing method based on a polynomial interpolation algorithm to protect data privacy and security of any user group. Background technique [0002] The secure calculation of the sum and product of any user has a very wide application value. With the development of network technology, personal data is often used for the calculation of statistical information or applications based on data mining. For example, the electricity dispatching in the smart grid uses the electricity consumption information of each household; the data mining in the social network involves personal information; the service provision in the crowdsourcing (Crowd Sourcing) application is based on the personal information provided by the public users. However, user data is sensitive, and users are unwilling to disclose their own data. Therefore, it is necessary to complet...

AI Technical Summary

Problems solved by technology

In the environment of actual application scenarios, the user group participating in computing often changes dynamically, so a certain user group cannot be fixed; there may be tens of thousands of users participating in computing, and the computing power and storage capacity of personal computing platforms are limited, making computing complex The degree and communication complexity should not be too large; many applications have relatively high requirements for calculation results, so approximate results cannot be used to replace the final calculation results; trusted third parties or trusted centers are difficult to exist in actual application scenarios, so calculations cannot rely on these object

After taking these environmental factors into account, most methods in the existing technology (such as secure multi-party computation, homomorphic encryption algorithm and other research results in the same field) cannot be applied in real life

Especially in the algorithms that need to get correct results in an environment without trusted channels, the related technologies so far need to generate a set of keys for each combined user, resulting in each user needing to save a 2 n key, the space complexity is too high

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