Aggregation Encryption Method of Social Network Privacy Data Based on Matrix Decomposition Algorithm

Currently, there are some defects in data aggregation encryption methods, such as low accuracy and efficiency. In this regard, this paper proposes an aggregation encryption method of social network privacy data based on matrix decomposition. Shamir threshold segmentation scheme was used to improve the security of matrix decomposition results, and then re-encrypted network user attributes. For codomain and non-codomain users, private file and key generation technology were used to improve the privacy of users in common domain and non-common domain. Considering the results of matrix decomposition, privacy security and efficient user matching in social networks were implemented. The node information was embedded in the process of data encryption and signature, and the interference factors were classified by cluster as a unit, which can resist the internal attack and improve the operation efficiency. The experimental results show that this method has excellent encryption effect and high privacy data protection processing efficiency.