Blocking confusion based dynamic data privacy protection system and method

Abstract

The invention discloses a blocking confusion based dynamic data privacy protection system and method. Newly inserted and modified data is cached through a trusted third party, and the data is grouped and stored when conditions are met; the privacy security of deleted data and residual data in a delete operation is ensured by keeping key fragmentations; and the reduction of storage resource consumption and the optimization of the application performance are realized by falsification of a data recycling mechanism. Experiments prove that the proposed dynamic data privacy protection mechanism is higher in feasibility and practicability.

Description

technical field [0001] The invention relates to a dynamic data privacy protection system and method based on block obfuscation. Background technique [0002] With the rapid development of cloud computing, SaaS applications with multi-tenant characteristics are adopted by more and more enterprises and service providers due to their low-cost, scale-effective business operation model and single-instance, on-demand software delivery features. . [0003] In SaaS applications, on the one hand, tenants meet their own business needs through on-demand leasing and personalized customization, while saving high costs for infrastructure and subsequent upgrades, maintenance, and management. On the other hand, by signing a service level agreement (service level agreement, SLA) with the SaaS service provider, the service quality of the application is guaranteed, and the interests of both the tenant and the service provider are maintained. [0004] However, with the widespread promotion an...

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Problems solved by technology

[0016] 2) Inhomogeneous block data leads to leakage of tenant privacy

[0020] 3) The problem of spurious data surge caused by block equalization

At this time, the ratio of forged data to the total data volume will reach 1 / 2. In the cloud computing environment, with the sharp increase of user data volume, a high proportion of forged data will not only waste a lot of storage space, but also lead to A drastic drop in application performance

On the other hand, work [1 ZhangKun, Li Qingzhong, Shi Yuliang. Research on Data Combination Privacy Preservation Mechanism for SaaS [J]. Chinese Journal of Computers, 2010, 33(11): 2044-2054 (in Chinese) [J].Journal of Computer Science,2010,33(11):2044-2054)] The proposed balance strategy is achieved by continuously inserting fake fragments. As the application continues to run, the fake data in the system will always increase, while When the amount of data is large, if you want to achieve data balance by deleting fake data, you will need to pay a huge computational cost

[0022] From the above example analysis, we can see that if the tenant's business operations are directly applied to the cloud data, not only will the private information of the tenant being operated this time be directly leaked, but also other private data in the table will be caused due to the data distribution in the block. Leakage due to unevenness

In addition, an unreasonable balance mechanism will also cause the system to pay a large storage consumption and performance cost due to the high proportion of forged data.

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