Multidimensional data provides enormous opportunities in a variety of applications. Recent research has indicated the failure of existing sanitization techniques （e.g., k-anonymity） to provide rigorous privacy guara...Multidimensional data provides enormous opportunities in a variety of applications. Recent research has indicated the failure of existing sanitization techniques （e.g., k-anonymity） to provide rigorous privacy guarantees. Privacy- preserving multidimensional data publishing currently lacks a solid theoretical foundation. It is urgent to develop new techniques with provable privacy guarantees, e-Differential privacy is the only method that can provide such guarantees. In this paper, we propose a multidimensional data publishing scheme that ensures c-differential privacy while providing accurate results for query processing. The proposed solution applies nonstandard wavelet transforms on the raw multidimensional data and adds noise to guarantee c-differential privacy. Then, the scheme processes arbitrarily queries directly in the noisy wavelet- coefficient synopses of relational tables and expands the noisy wavelet coefficients back into noisy relational tuples until the end result of the query. Moreover, experimental results demonstrate the high accuracy and effectiveness of our approach.展开更多
Minable data publication is ubiquitous since it is beneficial to sharing/trading data among commercial companies and further facilitates the development of data-driven tasks.Unfortunately,the minable data publication ...Minable data publication is ubiquitous since it is beneficial to sharing/trading data among commercial companies and further facilitates the development of data-driven tasks.Unfortunately,the minable data publication is often implemented by publishers with limited privacy concerns such that the published dataset is minable by malicious entities.It prohibits minable data publication since the published data may contain sensitive information.Thus,it is urgently demanded to present some approaches and technologies for reducing the privacy leakage risks.To this end,in this paper,we propose an optimized sanitization approach for minable data publication(named as SA-MDP).SA-MDP supports association rules mining function while providing privacy protection for specific rules.In SA-MDP,we consider the trade-off between the data utility and the data privacy in the minable data publication problem.To address this problem,SA-MDP designs a customized particle swarm optimization(PSO)algorithm,where the optimization objective is determined by both the data utility and the data privacy.Specifically,we take advantage of PSO to produce new particles,which is achieved by random mutation or learning from the best particle.Hence,SA-MDP can avoid the solutions being trapped into local optima.Besides,we design a proper fitness function to guide the particles to run towards the optimal solution.Additionally,we present a preprocessing method before the evolution process of the customized PSO algorithm to improve the convergence rate.Finally,the proposed SA-MDP approach is performed and verified over several datasets.The experimental results have demonstrated the effectiveness and efficiency of SA-MDP.展开更多
Public data empower the development of digital economy.On the basis of conceptual definition and scope framing,the legal attributes of public data should be used to determine the attribution of the right to use,and to...Public data empower the development of digital economy.On the basis of conceptual definition and scope framing,the legal attributes of public data should be used to determine the attribution of the right to use,and to deduce the mechanism of utilization of public data with public nature as the logical starting point.Based on the current situa⁃tion,the use of public data faces multiple difficulties in terms of normative basis,boundary delimitation,procedural rules,and protection policies.Therefore,publicity should be the core principle and the principle of convenience should be deepened,so as to clarify the boundaries between the use of public data and the protection of personal information rights and interests as well as legitimate commercial behavior.As a single platform can hardly meet the requirements of use,it should optimize the service procedures to adapt to different stages of use,standardize the way of data use super⁃vision,and clarify the supervisory responsibilities of the government and the platform.展开更多
基金the National Basic Research Program of China under Grant 2013CB338004,Doctoral Program of Higher Education of China under Grant No.20120073120034,National Natural Science Foundation of China under Grants No.61070204,61101108,and National S&T Major Program under Grant No.2011ZX03002-005-01
文摘Multidimensional data provides enormous opportunities in a variety of applications. Recent research has indicated the failure of existing sanitization techniques （e.g., k-anonymity） to provide rigorous privacy guarantees. Privacy- preserving multidimensional data publishing currently lacks a solid theoretical foundation. It is urgent to develop new techniques with provable privacy guarantees, e-Differential privacy is the only method that can provide such guarantees. In this paper, we propose a multidimensional data publishing scheme that ensures c-differential privacy while providing accurate results for query processing. The proposed solution applies nonstandard wavelet transforms on the raw multidimensional data and adds noise to guarantee c-differential privacy. Then, the scheme processes arbitrarily queries directly in the noisy wavelet- coefficient synopses of relational tables and expands the noisy wavelet coefficients back into noisy relational tuples until the end result of the query. Moreover, experimental results demonstrate the high accuracy and effectiveness of our approach.
基金This work was supported in part by the National Natural Science Foundation of China(No.61932006)in part by National Key R&D Program of China(No.2018AAA0100101)in part by Chongqing Technology Innovation and Application Development Project(No.cstc2020jscx-msxmX0156).
文摘Minable data publication is ubiquitous since it is beneficial to sharing/trading data among commercial companies and further facilitates the development of data-driven tasks.Unfortunately,the minable data publication is often implemented by publishers with limited privacy concerns such that the published dataset is minable by malicious entities.It prohibits minable data publication since the published data may contain sensitive information.Thus,it is urgently demanded to present some approaches and technologies for reducing the privacy leakage risks.To this end,in this paper,we propose an optimized sanitization approach for minable data publication(named as SA-MDP).SA-MDP supports association rules mining function while providing privacy protection for specific rules.In SA-MDP,we consider the trade-off between the data utility and the data privacy in the minable data publication problem.To address this problem,SA-MDP designs a customized particle swarm optimization(PSO)algorithm,where the optimization objective is determined by both the data utility and the data privacy.Specifically,we take advantage of PSO to produce new particles,which is achieved by random mutation or learning from the best particle.Hence,SA-MDP can avoid the solutions being trapped into local optima.Besides,we design a proper fitness function to guide the particles to run towards the optimal solution.Additionally,we present a preprocessing method before the evolution process of the customized PSO algorithm to improve the convergence rate.Finally,the proposed SA-MDP approach is performed and verified over several datasets.The experimental results have demonstrated the effectiveness and efficiency of SA-MDP.
基金Key Project of Scientific Research in Universities in Anhui Province"Legislation Research on Intellectual Property Protection of Data"(2022AH050023)。
文摘Public data empower the development of digital economy.On the basis of conceptual definition and scope framing,the legal attributes of public data should be used to determine the attribution of the right to use,and to deduce the mechanism of utilization of public data with public nature as the logical starting point.Based on the current situa⁃tion,the use of public data faces multiple difficulties in terms of normative basis,boundary delimitation,procedural rules,and protection policies.Therefore,publicity should be the core principle and the principle of convenience should be deepened,so as to clarify the boundaries between the use of public data and the protection of personal information rights and interests as well as legitimate commercial behavior.As a single platform can hardly meet the requirements of use,it should optimize the service procedures to adapt to different stages of use,standardize the way of data use super⁃vision,and clarify the supervisory responsibilities of the government and the platform.
