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Recently, mobile cloud computing has been named as the top one emerging technology in 2014 by IEEE Computer Society. This brings a strong demand on new emergent mobile data service solutions and technologies in the wireless world and implies that more innovative mobile data service solutions are needed to support on-demand elastic and large-scale mobile data service requests. This paper focuses on...
With large elastic and scalable infrastructures, the Cloud is the ideal storage repository for Big Data applications. Big Data is typically characterized by three V's: Volume, Variety and Velocity. Supporting these properties raises significant challenges in a cloud setting, including partitioning for scale out; replication across data centers for fault-tolerance; significant latency overheads due...
The field of privacy pursues rapid advances in recent years because of the increases in the ability to store data. One of the most important topics in research community is Privacy preserving data mining (PPDM). Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. People today have become well aware of the privacy...
In DaaS, users lose physical control over their data, and the DSP which is not fully trusted own all the operating rights over the data, on this service model the data owners are seriously worried about their privacy data. Based on the related research work, a data partition method based on attribute similarity for privacy protection was proposed in this paper. This method partition attribute into...
Cloud computing has brought many advantages to companies and computer users. It allows different service providers to distribute many applications as services in an economical way. Therefore, many users and companies have begun using cloud computing. However, they are concerned about their data when they store it on a third party, the cloud. Fears of leakage of sensitive data or loss of privacy make...
Cognitive Radio Networks (CRNs) has been considered as a key technology for future wireless communications and mobile computing. Localization of primary user is crucial in enabling several key capabilities in CRNs. In this paper, we present a brief survey of representative methods dealing with user localization and location privacy preservation issues. The objective is to provide a comprehensive analysis...
In era of information age, due to different electronic, information & communication technology devices and process like sensors, cloud, individual archives, social networks, internet activities and enterprise data are growing exponentially. The most challenging issues are how to effectively manage these large and different type of data. Big data is one of the term named for this large and...
The aim of the Nu.Sa. project is the definition of national level data standards to collect data coming from General Practitioners' Electronic Health Records and to allow secure data sharing between them. This paper introduces the Nu.Sa. framework and is mainly focused on security issues. A solution for secure data sharing and service interoperability is presented and implemented in the actual system...
Pk-anonymization is a data anonymization method that employs randomization. Pk-anonymization guarantees Pk-anonymity, which is an extension of probabilistic k-anonymity. To implement this method, we assign random noise to records to reduce the probability of identifying record owners to less than 1/k. Existing methods assign noise using a Laplace distribution, and determine the variance of the Laplace...
With the advancement in technology, industry, e-commerce and research a large amount of complex and pervasive digital data is being generated which is increasing at an exponential rate and often termed as big data. Traditional Data Storage systems are not able to handle Big Data and also analyzing the Big Data becomes a challenge and thus it cannot be handled by traditional analytic tools. Cloud Computing...
Privacy protection issues in Social Networking Sites (SNS) usually raise from insufficient user privacy control mechanisms offered by service providers, unauthorized usage of user's data by SNS, and lack of appropriate privacy protection schemes for user's data at the SNS servers. In this paper, we propose a privacy protection model based on danger theory concept to provide automatic detection and...
Security and privacy are the biggest obstacles in Database as a service (DBaaS) of Cloud Computing. In DbaaS, cloud service providers provide services for storing customers data. As the data are managed by an un-trusted server, the service is not fully trustworthy. The data at the third party data center can be made secure by encrypting the database. But querying the encrypted database is not easy...
This paper deals with the problem of privacy issues caused by the tracking activities of web advertisers. Web advertisers place cookies in the browser to track users' surfing behaviour across websites, mostly without his knowledge or consent. This paper proposes a fine-grained, per-site cookie management protocol. We implement our proposal as Mozilla Firefox add-on and we call it TTPCookie. We evaluate...
Social scientists who collect large amounts of medical data value the privacy of their survey participants. As they follow participants through longitudinal studies, they develop unique profiles of these individuals. A growing challenge for these researchers is to maintain the privacy of their study participants, while sharing their data to facilitate research. Differential privacy is a new mechanism...
Personal mobile data are being extensively collected by various service providers, in the form of data stream. Most service providers promise their customers for not misusing their data by paper-based agreement. However, the customers have no way to know whether the agreements are strictly followed or not, unless any scandals of private data misuse are revealed. To guarantee the correct use of customers'...
Differential privacy is becoming a gold standard notion of privacy, it offers a guaranteed bound on loss of privacy due to release of query results, even under worst-case assumptions. The theory of differential privacy is an active research area, and there are now differentially private algorithms for a wide range of problems. However, the question of when differential privacy works in practice has...
Differential privacy is a rigorous, worst-case notion of privacy-preserving computation. Informally, a probabilistic program is differentially private if the participation of a single individual in the input database has a limited effect on the program's distribution on outputs. More technically, differential privacy is a quantitative 2-safety property that bounds the distance between the output distributions...
This seminar overviews recent research on novel data models for the advanced representation of mobility data encompassing semantic and contextual information.
Personalization has been implemented in a variety of services. k-anonymity is the most common definition for the anonymization of personal data sets, and is considered to be a normal feature of personal data exchanges. However, there is an important issue: How to find an appropriate k for k-anonymity. In this paper, we present a model for finding an appropriate k in k-anonymization. The model suggests...
The significant development in field of data collection and data storage technologies have provided transactional data to grow in data warehouses that reside in companies and public sector organizations. As the data is growing day by day, there has to be certain mechanism that could analyze such large volume of data. Data mining is a way of extracting the hidden predictive information from those data...
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