Research On The Key Technologies Of Data Privacy Preservation In Outsourced Computing

With the development and popularization of intelligent devices,the massive intelligent terminal devices connected to the network have produced large-scale operation data,and the scale of data services has grown explosively,which has brought great pressure on data management to local users with limited resources.Based on the outsourcing service mode of data storage and computing provided by cloud computing services,more and more users are willing to outsource their data to the cloud service platform for management.Specifically,in cloud computing,services are outsourced through storage space and computing capacity provided by cloud service providers.Users can purchase storage space and computing services from cloud service providers according to their data scale and computing tasks,which can not only make full use of data resources but also reduce the cost of computing for users.Users can also access and retrieve their data anytime and anywhere.Just because of the powerful advantages,cloud computing,as a kind of simple and practical technique that can meet the needs of outsourced computing,promotes the rapid development of outsourced computing.However,while enjoying the convenience of outsourced computing,many data security and privacy issues have to be addressed.Firstly,there is a lack of data availability as well as some information leakage issues during the process of graph computation.Secondly,in verifiable computing,there is little research on graph intersection algorithms,which do not support verification of the results of cloud server computing.Finally,in the process of data aggregation,a trusted third party is required,which makes it difficult to resist collusion attacks.To address the above challenges,this paper delves into the data security and data integrity verification problems in the outsourced environment,focusing on three core research points for outsourced services,namely,a privacy-protecting subgraph matching scheme,a privacy-protecting verifiable graph intersection scheme,and a data aggregation scheme.Details of the research are shown below.(1)Aiming at the low data availability and certain information leakage problems of the current privacy-preserving subgraph matching schemes,this paper proposes a privacy-preserving subgraph matching scheme for outsourcing computing.In this scheme,by introducing the cloud service platform as an intermediate entity into the privacy protection subgraph matching scheme,the subgraph matching calculation task and the graph storage task are entrusted to the cloud service platform,so that the cloud service platform completes the subgraph matching under the ciphertext,and the cloud service can not obtain the data privacy of users.The message verification code technology is used to complete data integrity verification and user identity verification so that each receiver can verify whether the received message comes from a legitimate sender and has not been tampered with.Compared with existing sub-graph matching schemes,the sub-graph matching schemes in this paper has higher efficiency in terms of computational cost and communication cost.Therefore,the proposed scheme not only protects the privacy of graph data but also has higher computational efficiency.The proposed scheme is better applicable to the application scenarios of subgraph matching in social networks.(2)Aiming at the problem that the current privacy-preserving data verifiable scheme does not support graph intersection verification,this paper proposes a privacy-oriented verifiable graph intersection scheme for outsourcing computing.This solution outsources graph data storage tasks and computing tasks to the cloud platform,reduces the computing and storage costs of local users,and ensures that the cloud service platform completes graph intersection calculations and cannot obtain any user's graph information.Additionally,when the cloud service platform is untrusted or compromised by some adversaries,the results that the cloud service platform returns can not be guaranteed to be correct.In this paper,the requester uses the bilinear-map accumulator to verify the correctness of graph intersection results that the cloud service platform returns,which ensures the correctness of the returned results and forms an outsourcing mechanism that supports privacy and integrity protection.Therefore,this solution can be applied to find common friends,blood relationships,etc.of social users,but does not reveal the user's social information.(3)Aiming at the current privacy-preserving multi-dimensional data aggregation scheme,a trusted third party is required to distribute aggregation parameters and cannot resist collusion attacks.This paper proposes a privacy protection scheme for multi-dimensional multi-set data aggregation for outsourcing computing.This solution outsources the data of local users to the aggregation node,which realizes data aggregation under ciphertext,and the control center realizes the analysis of the aggregation data.The distributed encryption and decryption method is used to realize the distribution of aggregation parameters without introducing a trusted third party,and it can resist the collusion attack of untrusted user nodes.Using super-increasing sequences to diversify data analysis can not only sum each type of data but also can perform interval analysis on the cumulative value of each user's data.By constructing data integrity verification and user legitimacy mechanism,it is ensured that when the user interacts with the aggregation node and the control center,the user's data is not tampered with and the receiver can verify the legitimacy of the sender's identity.Theoretical analysis and experimental simulation verify the safety and effectiveness of this scheme.