Privacy preservation has recently received considerable attention in location-based services （LBSs）. A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile us...Privacy preservation has recently received considerable attention in location-based services （LBSs）. A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile users. However, most existing cloaking approaches assume that mobile users are trusted. And exact locations are required to protect location privacy, which is exactly the information mobile users want to hide. In this paper, we propose a p-anti-conspiration privacy model to anonymize over semi-honest users. Further- more, two k＊NNG-based cloaking algorithms, vk＊NNCA and ek＊NNCA, are proposed to protect location privacy without exact locations. The efficiency and effectiveness of the pro- posed algorithms are validated by a series of carefully designed experiments. The experimental results show that the price paid for location privacy protection without exact locations is small.展开更多
Frequent pattern mining discovers sets of items that frequently appear together in a transactional database; these can serve valuable economic and research purposes. However, if the database contains sensitive data （...Frequent pattern mining discovers sets of items that frequently appear together in a transactional database; these can serve valuable economic and research purposes. However, if the database contains sensitive data （e.g., user behavior records, electronic health records）, directly releas- ing the discovered frequent patterns with support counts will carry significant risk to the privacy of individuals. In this pa- per, we study the problem of how to accurately find the top-k frequent patterns with noisy support counts on transactional databases while satisfying differential privacy. We propose an algorithm, called differentially private frequent pattern （DFP- Growth）, that integrates a Laplace mechanism and an expo- nential mechanism to avoid privacy leakage. We theoretically prove that the proposed method is （λ, δ）-useful and differ- entially private. To boost the accuracy of the returned noisy support counts, we take consistency constraints into account to conduct constrained inference in the post-processing step. Extensive experiments, using several real datasets, confirm that our algorithm generates highly accurate noisy support counts and top-k frequent patterns.展开更多
Cell behavior is affected by nanostructured surface,but it remains unknown how ionizing radiation af-fects cells on nanostructured surface.This paper reports an experimental investigation of X-ray radiation induced da...Cell behavior is affected by nanostructured surface,but it remains unknown how ionizing radiation af-fects cells on nanostructured surface.This paper reports an experimental investigation of X-ray radiation induced damage of cells placed on an array of vertically aligned silicon nanowires.X-ray photoelectrons and secondary electrons produced from nanowire array are measured and compared to those from flat silicon substrate.The cell functions including morphology,viability,adhesion and proliferation have been examined and found to be drastically affected when cells are exposed to X-ray radiation,compared to those sitting on flat substrate and those only exposed to X-ray.The enhanced cell damage on nanowires upon X-ray exposure is attributed to nanowire enhanced production of photoelectrons including Auger electrons and secondary electrons,which have high escaping probability from sharp tips of nanowires.The escaped photoelectrons ionize water molecules and generate hydroxyl free radicals that can damage DNAs of cells.An inference of this work is that the contrast in scanning electron microscopy is useful in assessing the effects of nanomaterials for enhanced X-ray radiation therapy.展开更多
In location-based services, a density query re- turns the regions with high concentrations of moving objects （MOs）. The use of density queries can help users identify crowded regions so as to avoid congestion. Most ...In location-based services, a density query re- turns the regions with high concentrations of moving objects （MOs）. The use of density queries can help users identify crowded regions so as to avoid congestion. Most of the exist- ing methods try very hard to improve the accuracy of query results, but ignore query efficiency. However, response time is also an important concern in query processing and may have an impact on user experience. In order to address this issue, we present a new definition of continuous density queries. Our approach for processing continuous density queries is based on the new notion of a safe interval, using which the states of both dense and sparse regions are dynamically main- tained. Two indexing structures are also used to index candi- date regions for accelerating query processing and improving the quality of results. The efficiency and accuracy of our approach are shown through an experimental comparison with snapshot density queries.展开更多
Recently,the problem of mobile applications(Apps)leaking users’private information has aroused wide concern.As the number of Apps continuously increases,effective large-scale App governance is a major challenge.Curre...Recently,the problem of mobile applications(Apps)leaking users’private information has aroused wide concern.As the number of Apps continuously increases,effective large-scale App governance is a major challenge.Currently,the government mainly filters out Apps with potential privacy problems manually.Such approach is inefficient with limited searching scope.In this regard,we propose a quantitative method to filter out problematic Apps on a large scale.We introduce Privacy Level(P-Level)to measure an App’s probability of leaking privacy.P-Level is calculated on the basis of Permission-based Privacy Value(P-Privacy)and Usage-based Privacy Value(U-Privacy).The former considers App permission setting,whereas the latter considers App usage.We first illustrate the privacy value model and computation results of both values based on real-world dataset.Subsequently,we introduce the P-Level computing model.We also define the P-Level computed on our dataset as the PL standard.We analyze the distribution of average usage and number of Apps under the levels given in the PL standard,which may provoke insights into the large-scale App governance.Through P-Privacy,U-Privacy,and P-Level,potentially problematic Apps can be filtered out efficiently,thereby making up for the shortcoming of being manual.展开更多
基金This research was partially supported by the grant from the Hebei Education Department （Q2012131 and SKZD2011113）, and the National Natural Science Foundation of China （Grant No. 61070055）.
文摘Privacy preservation has recently received considerable attention in location-based services （LBSs）. A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile users. However, most existing cloaking approaches assume that mobile users are trusted. And exact locations are required to protect location privacy, which is exactly the information mobile users want to hide. In this paper, we propose a p-anti-conspiration privacy model to anonymize over semi-honest users. Further- more, two k＊NNG-based cloaking algorithms, vk＊NNCA and ek＊NNCA, are proposed to protect location privacy without exact locations. The efficiency and effectiveness of the pro- posed algorithms are validated by a series of carefully designed experiments. The experimental results show that the price paid for location privacy protection without exact locations is small.
基金This research was partially supported by the National Natural Science Foundation of China （Grant Nos. 61379050, 91224008）, the National 863 High-tech Program （2013AA013204）, Specialized Research Fund for the Doctoral Program of Higher Education（20130004130001）
文摘Frequent pattern mining discovers sets of items that frequently appear together in a transactional database; these can serve valuable economic and research purposes. However, if the database contains sensitive data （e.g., user behavior records, electronic health records）, directly releas- ing the discovered frequent patterns with support counts will carry significant risk to the privacy of individuals. In this pa- per, we study the problem of how to accurately find the top-k frequent patterns with noisy support counts on transactional databases while satisfying differential privacy. We propose an algorithm, called differentially private frequent pattern （DFP- Growth）, that integrates a Laplace mechanism and an expo- nential mechanism to avoid privacy leakage. We theoretically prove that the proposed method is （λ, δ）-useful and differ- entially private. To boost the accuracy of the returned noisy support counts, we take consistency constraints into account to conduct constrained inference in the post-processing step. Extensive experiments, using several real datasets, confirm that our algorithm generates highly accurate noisy support counts and top-k frequent patterns.
基金supported by a Director’s New Innovator Award from National Institute of Health(No.1DP2EB016572).
文摘Cell behavior is affected by nanostructured surface,but it remains unknown how ionizing radiation af-fects cells on nanostructured surface.This paper reports an experimental investigation of X-ray radiation induced damage of cells placed on an array of vertically aligned silicon nanowires.X-ray photoelectrons and secondary electrons produced from nanowire array are measured and compared to those from flat silicon substrate.The cell functions including morphology,viability,adhesion and proliferation have been examined and found to be drastically affected when cells are exposed to X-ray radiation,compared to those sitting on flat substrate and those only exposed to X-ray.The enhanced cell damage on nanowires upon X-ray exposure is attributed to nanowire enhanced production of photoelectrons including Auger electrons and secondary electrons,which have high escaping probability from sharp tips of nanowires.The escaped photoelectrons ionize water molecules and generate hydroxyl free radicals that can damage DNAs of cells.An inference of this work is that the contrast in scanning electron microscopy is useful in assessing the effects of nanomaterials for enhanced X-ray radiation therapy.
文摘In location-based services, a density query re- turns the regions with high concentrations of moving objects （MOs）. The use of density queries can help users identify crowded regions so as to avoid congestion. Most of the exist- ing methods try very hard to improve the accuracy of query results, but ignore query efficiency. However, response time is also an important concern in query processing and may have an impact on user experience. In order to address this issue, we present a new definition of continuous density queries. Our approach for processing continuous density queries is based on the new notion of a safe interval, using which the states of both dense and sparse regions are dynamically main- tained. Two indexing structures are also used to index candi- date regions for accelerating query processing and improving the quality of results. The efficiency and accuracy of our approach are shown through an experimental comparison with snapshot density queries.
基金This work was partially supported by the National Natural Science Foundation ofChina(Grant Nos.62172423,91846204,and 61941121).
文摘Recently,the problem of mobile applications(Apps)leaking users’private information has aroused wide concern.As the number of Apps continuously increases,effective large-scale App governance is a major challenge.Currently,the government mainly filters out Apps with potential privacy problems manually.Such approach is inefficient with limited searching scope.In this regard,we propose a quantitative method to filter out problematic Apps on a large scale.We introduce Privacy Level(P-Level)to measure an App’s probability of leaking privacy.P-Level is calculated on the basis of Permission-based Privacy Value(P-Privacy)and Usage-based Privacy Value(U-Privacy).The former considers App permission setting,whereas the latter considers App usage.We first illustrate the privacy value model and computation results of both values based on real-world dataset.Subsequently,we introduce the P-Level computing model.We also define the P-Level computed on our dataset as the PL standard.We analyze the distribution of average usage and number of Apps under the levels given in the PL standard,which may provoke insights into the large-scale App governance.Through P-Privacy,U-Privacy,and P-Level,potentially problematic Apps can be filtered out efficiently,thereby making up for the shortcoming of being manual.
