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Together with the development of technologies such as those for ubiquitous computing, data mining, Internet of Things (IoT) and wireless sensor networks (WSNs), the concepts of smart cities and mobile health (m-Health) have emerged. Along the same line, the smart health concept (s-Health), understood as a context-aware healthcare paradigm for smart environments, improves the quality of healthcare...
Differential privacy has seen dramatic development in recent decades as data mining of the statistical private datasets in a distributed big data environment has become an effective paradigm that, it is argued, guarantees the mathematically rigorous privacy of the participants by ensuring the equivalence of the analyzing results with the removal or addition of a single database item. However, challenges...
In today's digital world, with the increase of using internet, personal data is collected with online activities for analysis or for surveillance. So there is a need of providing privacy to the personal data. In many organizations the sensitive data is shared with authorized access, but there is still disclosure of identity of persons. So privacy is a major concern. Privacy for the sensitive data...
Health Information is considered the most sensitive information associated to an individual. Even though numerous suitable policies, guidelines, and compliance requirements are in place to safeguard health information, privacy and security breach remains key issues for electronic healthcare systems. In this paper we focus on these issues and propose a security and privacy model implemented in Methodist...
In fault-prone large-scale distributed environments stochastic gradient descent (SGD) is a popular approach to implement machine learning algorithms. Data privacy is a key concern in such environments, which is often addressed within the framework of differential privacy. The output quality of differentially private SGD implementations as a function of design choices has not yet been thoroughly evaluated...
In fault-prone large-scale distributed environments stochastic gradient descent (SGD) is a popular approach to implement machine learning algorithms. Data privacy is a key concern in such environments, which is often addressed within the framework of differential privacy. The output quality of differentially private SGD implementations as a function of design choices has not yet been thoroughly evaluated...
Online Social Network (OSN) has become a popular and de facto portal for digital information sharing. Preserving information privacy is indispensable in such applications, as the shared information would be sensitive. The issue becomes more challenging due to participation of multiple parties on the shared resources or data. In this paper, we propose an effective access control technique to allow...
The crimes in the social networking sites are increasing day by day. One of the main crimes is misusing the image by morphing and using it for blackmailing. Users can view the image and edit the image. On social networking sites, users post their own pictures and these pictures can be viewed by anyone. Anyone can access the image, whether the users are authorized or unauthorized. There is no centralized...
Growth of computing and analytics, internet and distributed computing, and increasing tendency to use crowd sourcing for creative tasks, the use of collaborative idea in information intensive activities is a growing research concern. In CIS companies would cooperate to do their interlinked business together. It responds to customer requests for information retrieval from or writing on data contents...
With the popularity of social networks such as Facebook and twitter, social recommendations have become possible, which rely on individual's social connections in order to make personalized recommendations of ads, content, products, and people. Since recommendations involving sensitive information, adversaries may re-identify a user's sensitive information from the recommendation results using background...
Automated human facial image de-identification is a much needed technology for privacy-preserving social media and intelligent surveillance applications. Other than the usual face blurring techniques, in this work, we propose to achieve facial anonymity by slightly modifying existing facial images into "averaged faces" so that the corresponding identities are difficult to uncover. This approach...
Protection of location privacy by reducing the accuracy of location data, until a desired level of privacy (e.g., measured as k-anonymity) is reached, is a well-known concept that is typically implemented using a privacy proxy. To eliminate the risks associated with a central, trusted party, we propose a generic method to enforce k-anonymity of location data in a decentralized way, using a distributed...
Privacy-preserving data publishing has been widely explored in academia recently. The state-of-the-art goal for data privacy-preserving is differential privacy, which offers a strong degree of privacy protection against adversaries with arbitrary background knowledge. However, along with a wide query scope in the non-interactive model like DiffGen, the accumulation of noise in the query answers can...
Private Information Retrieval (PIR) is the fact that a client retrieves a certain record from a remote database managed by untrusted parties without letting them know which record has actually been requested, thus preserving the privacy of the client. Ostrovsky and Skeith showed in [1] that any homomorphic encryption can actually be used to achieve PIR. In this paper we use their protocol as basis...
MOOCs have expanded educational access beyond the traditional scope and masses. Cloud computing, open source platforms and software frameworks have provided the MOOC elements to support the creation of cost effective infrastructures, minimizing costs with widespread accessibility. However, these foundational elements present security concerns, which include non-enforceable policies, system and software...
The characteristics of volume, variety, velocity and value for big data have made present privacy protection methods less effective in the protection of user privacy to meet the emerging requirements. In this paper, we analyze the spatial and temporal effects the access to the data on privacy disclosure and propose an access control model to protect user privacy that is related to the number and the...
Many organizations, including businesses, government agencies and research organizations, are collecting vast amounts of data, which are stored, processed and analyzed to mine interesting patterns and knowledge to support efficient and quality decision making. In order to improve data quality and to facilitate further analysis, many application domains require information from multiple sources to...
To assess the quality of hospital care, national databases of standard medical procedures are common. A widely known example are national databases of births. If unique personal identification numbers are available (as in Scandinavian countries), the construction of such databases is trivial from a computational point of view. However, due to privacy legislation, such identifiers are not available...
A common approach in crowdsensing projects consists in using the sensing capabilities of mobile's user devices to collect some data, which is later aggregated and processed by a central collector, and then published in aggregated form. In return, individual users obtain useful information related to the provided data, making the exchange mutually beneficial. This use of crowds provides a framework...
The focus on k-anonymity enhancements along the last decade, allows this method to be elected as the point of start for many anonymization researches. In this paper we propose « Contra-Loss », a novel anonymization approach applied at internal level, to enhance local privacy where data is at rest and « Flexible k- anonymity » which aims to apply k-anonymity in most situations by defining a semantic...
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