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The categorical data that have natural ordering between categories are termed ordinal data, which are pervasive in numerous areas, including discrete sensor readings, metering data or preference options. Though aggregating such ordinal data from the population is facilitating plenty of crowdsourcing applications, contributing such data is privacy risky and may reveal sensitive information (e.g. locations,...
With the increasing prevalence of cloud computing, data owners prefer to outsource their databases to the cloud. For the protection of data privacy, sensitive data have to be encrypted before outsourcing, which introduces much difficulty into effective data utilization. Most previous studies either suffer from privacy disclosure and low efficiency, or do not support personalized multidimensional range...
Mobile crowdsensing applications can learn the aggregate statistics over personal data to produce useful knowledge about the world. Since personal data may be privacy-sensitive, the aggregator should only gain desired statistics without learning anything about the personal data. Differential privacy, the state-of-the-art privacy mechanism, can provide strong protection to ensure parties' privacy in...
Histogram estimation is one of the fundamental tasks in crowdsourcing data aggregation. Since contributing data reveal more or less information about individuals' identifications and activities, participants need to preserve privacy of data according to their own levels of privacy concern. However, most of the existing work only aggregates data with an identical privacy level. In this paper, we propose...
Because of the large volume and quick-change of real-time electricity data, users' data generally is stored in various cloud platforms, which are easily attacked by adversaries, and users' privacy is revealed by this disclosure of data indirectly. Therefore, in order to prevent the disclosure of privacy, it is necessary for cloud platforms to have an efficient, secure method to protect the real-time...
Outlier detection can find its tremendous applications in areas such as intrusion detection, fraud detection, and image processing. Among many outlier detection algorithms, LOF is a very important density-based algorithm in which one critical step is to find the k-distance neighbors. In some privacy preserving circumstances, the cooperation between data holders is necessary while the privacy of the...
Privacy-preserving data mining aims at securely extracting knowledge from two or more parties' private data. Secure multi-party computation is the paramount approach to it. In this paper, we study privacy-preserving add and multiply exchanging technology and present three new different approaches to privacy-preserving add to multiply protocol. After that, we analyze and compare the three different...
Convex Hulls Problem is a special case of Privacy-preserving Geometry problems in the inquiry of Secure Multi-Party Computation (SMC). It can be applied in military, commercial and many other fields. However, because of the definition's inherent defect, current schemes will inevitably disclose the points on the vertices. In this paper, we proposed the concept of privacy-preserving approximate convex...
Secure multi-party computation has been a hot research topic of cryptograhy for about two decades, and the convex hulls problem is a special case of it. However, the precise convex hulls will certainly expose all vertexes and even bring about unfairness. Therefore the practical approximate convex hulls are in need. In this paper, we summarize and discuss the convex hulls problem, and then we present...
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