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This paper presents a novel framework for privacy aware collaborative information sharing for data classification. Data holders participating in this information sharing system, for global benefits are interested to model a classifier on whole dataset, but are ready to share their own table of data if a certain amount of privacy is guaranteed. To address this issue, we propose a privacy mechanism...
Machine learning is widely used in practice to produce predictive models for applications such as image processing, speech and text recognition. These models are more accurate when trained on large amount of data collected from different sources. However, the massive data collection raises privacy concerns. In this paper, we present new and efficient protocols for privacy preserving machine learning...
For single-owner multi-user wireless sensor networks, there is the demand to implement the user privacy-preserving access control protocol in WSNs. Firstly, we propose a new access control protocol based on an efficient attribute-based signature. In the protocol, users need to pay for query, and the protocol achieves fine-grained access control and privacy protection. Then, the protocol is analyzed...
The Location-Based Services (LBSs) have attracted a lot of attention in recent years. For privacy concerns, there are abundant works focusing on the secure query over the location server. However, these works suffer from two main limitations. First, they cannot preserve the location privacy and content privacy simultaneously during LBS queries. Second, they do not support the ranked queries. In this...
Cloud computing can be considered a new computing paradigm due to its diverse benefits such as the sharing of computing resources and the data containing personal, information across multiple distributed and private databases. However, privacy and security concerns are among the main obstacles facing the widespread adoption of this new technology. Data Anonymization makes data worthless to anyone...
Service discovery in global software markets is performed by brokers who act as intermediaries between service requesters and service providers. In order to discover services, brokers apply service matching for determining whether the specification of a provided service satisfies the requester’s requirements. Brokers can already choose between a lot of different service matching approaches considering...
Privacy preserving is one of the most important research topics in the data security field and it has become a serious concern in the secure transformation of personal data in recent years. A number of algorithmic techniques have been designed for Privacy Preserving Data Mining (PPDM). It is used to efficiently protect individual privacy in data sharing. Thus, the various models have been designed...
Data from social networks are an excellent source of information for studying human behaviors and interactions. Typically, when analyzing such data, the default mode of access is de-identified data, which provides a level of privacy protection. However, due to its inability to link to other data, de-identified data has limitations with regard to answering broad and critically important questions about...
Recently, a new research area, named Privacy-preserving Distributed Data Mining (PPDDM) has emerged. It aims at solving the following problem: a number of participants want to jointly conduct a data mining task based on the private data sets held by each of the participants. This problem setting has captured attention and interests of researchers, practitioners and developers from the communities...
In recent years more and more studies are focused on trust management of vehicle ad-hoc networks (VANETs). However, little attention has been given to the issue of location privacy of the existing trust methodologies in the literature. Although traffic safety remains to be the most crucial issue in VANETs, location privacy can be just as important for drivers, and neither of them can be ignored. In...
Collection and analysis of personal information is among the most far-reaching developments in online retail practices. While the potential value of harnessing data about people is expected to improve the online service offerings, it raises reasonable concerns about privacy. Rather than cutting off opportunities to make personal data available for enhancing online services, we introduce a model where...
Several vulnerability analysis techniques in web-based applications detect and report on different types of vulnerabilities. However, no single technique provides a generic technology-independent handling of Web-based vulnerabilities. In this paper we present our experience with and experimental exemplification of using the application vulnerability description language (AVDL) to realize a unified...
As digital resources increasingly growing and the economic benefit of digital intellectual property rights being increasingly important, people has been increasingly emphasis on information security issues brought by the data remnants in storage devices. They try their best to prevent the potential risks. In this paper, we survey comprehensively related technologies, standards and trends of erasure,...
In this paper, we propose a DRM system to protect users' privacy. The proposed system utilizes special protocols designed to preserve privacy using pseudonyms and an obfuscation mechanism. Those provide different levels of controls on metered data and personal identifiers, by expressing their preference on privacy sensitive information.
Information privacy typically concerns the confidentiality of personal identifiable information (PII) and protected health information (PHI) such as electronic medical records. Thus, the information access control mechanism for e-health services must be embedded with privacy-enhancing technologies. Role-based access control (RBAC) model has been widely investigated and applied to various applications...
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