Research On Database Watermarking Technology For Big Data Sharing

The rapid development of computer network and big data technology provide much convenience for information sharing.Nowadays,various forms of data,such as audio,video,image and text,are published on the Internet.As the main carrier of data storage,database is facing urgent requirments of copyright proteciotn and privacy protection with the data security incidents being reported frequently around the world.Large number of research works have been proposed to protect the copyright and privacy of the database through watermarking and differential privacy,respectively;however,these works have the following shortcomings:(1)state-of-the-art database watermarking schemes introduce large data distortion when dealing with integer data,which cannot satify the utility requirement of data mining aolgorithms on the watermarked database in the circumtances of bigdata sharing;(2)in the scenario of growing popularity of big data transactions,database sharing needs to consider both the copyright protection and data privacy protection,while state-of-the-art database watermarking schemes cannot provide both copyright protection and data privacy protection at the same time.Aiming at these problems,this dissertation conducts the following research:Firstly,this dissertation designs a robust reversible database watermarking method with distortion control.According to the characteristics of small data distortion caused by histogram shifting,we design a novel histogram shifting reversible watermarking method.The genetic algorithm is used to optimize the watermarking method.According to the contradiction between the watermarking capacity and data distortion caused by watermarks,we design a fitness function of genetic algorithm so as to achieve the balance between watermarking capacity and data distortion.Secondly,this dissertation proposes and designs database watermarking methods based on local differential privacy.According to the different implementation mechanisms of local differential privacy,database watermarking methods based on Laplacian mechanism and randomized response mechanism are proposed respectively.The database watermarking methods utilize the large data redundancy caused by local differential privacy to embed the watermark,which reduce data distortion and improve data utility at the same time.Finally,this dissertation proves the effectiveness of reversible database watermarking method and the robustness of the watermark through a large number of experiments;and this dissertation proves the effectiveness and feasibility of the local differential privacy-based database watermarking methods,analyzes the impact of watermarking on privacy protection through theoretical proof and experimental results,as well as verifies the data utility and the advantages and disadvantages of each proposed watermarking method by comparative experiments.