Continuous response of range query on steaming data provides useful information for many practical applications as well as the risk of privacy disclosure.The existing research on differential privacy streaming data pu...Continuous response of range query on steaming data provides useful information for many practical applications as well as the risk of privacy disclosure.The existing research on differential privacy streaming data publication mostly pay close attention to boosting query accuracy,but pay less attention to query efficiency,and ignore the effect of timeliness on data weight.In this paper,we propose an effective algorithm of differential privacy streaming data publication under exponential decay mode.Firstly,by introducing the Fenwick tree to divide and reorganize data items in the stream,we achieve a constant time complexity for inserting a new item and getting the prefix sum.Meanwhile,we achieve time complicity linear to the number of data item for building a tree.After that,we use the advantage of matrix mechanism to deal with relevant queries and reduce the global sensitivity.In addition,we choose proper diagonal matrix further improve the range query accuracy.Finally,considering about exponential decay,every data item is weighted by the decay factor.By putting the Fenwick tree and matrix optimization together,we present complete algorithm for differentiate private real-time streaming data publication.The experiment is designed to compare the algorithm in this paper with similar algorithms for streaming data release in exponential decay.Experimental results show that the algorithm in this paper effectively improve the query efficiency while ensuring the quality of the query.展开更多
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
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This work is supported,in part,by the National Natural Science Foundation of China under grant numbers 61300026in part,by the Natural Science Foundation of Fujian Province under grant numbers 2017J01754, 2018J01797.
文摘Continuous response of range query on steaming data provides useful information for many practical applications as well as the risk of privacy disclosure.The existing research on differential privacy streaming data publication mostly pay close attention to boosting query accuracy,but pay less attention to query efficiency,and ignore the effect of timeliness on data weight.In this paper,we propose an effective algorithm of differential privacy streaming data publication under exponential decay mode.Firstly,by introducing the Fenwick tree to divide and reorganize data items in the stream,we achieve a constant time complexity for inserting a new item and getting the prefix sum.Meanwhile,we achieve time complicity linear to the number of data item for building a tree.After that,we use the advantage of matrix mechanism to deal with relevant queries and reduce the global sensitivity.In addition,we choose proper diagonal matrix further improve the range query accuracy.Finally,considering about exponential decay,every data item is weighted by the decay factor.By putting the Fenwick tree and matrix optimization together,we present complete algorithm for differentiate private real-time streaming data publication.The experiment is designed to compare the algorithm in this paper with similar algorithms for streaming data release in exponential decay.Experimental results show that the algorithm in this paper effectively improve the query efficiency while ensuring the quality of the query.
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.
