Topic
Information privacy
About: Information privacy is a research topic. Over the lifetime, 25412 publications have been published within this topic receiving 579611 citations. The topic is also known as: data privacy & data protection.
Papers published on a yearly basis
Papers
More filters
17 Oct 2011
TL;DR: A suite of new techniques that make such privacy-aware data-intensive computing possible, called Sedic, leverages the special features of MapReduce to automatically partition a computing job according to the security levels of the data it works on, and arrange the computation across a hybrid cloud.
Abstract: The emergence of cost-effective cloud services offers organizations great opportunity to reduce their cost and increase productivity. This development, however, is hampered by privacy concerns: a significant amount of organizational computing workload at least partially involves sensitive data and therefore cannot be directly outsourced to the public cloud. The scale of these computing tasks also renders existing secure outsourcing techniques less applicable. A natural solution is to split a task, keeping the computation on the private data within an organization's private cloud while moving the rest to the public commercial cloud. However, this hybrid cloud computing is not supported by today's data-intensive computing frameworks, MapReduce in particular, which forces the users to manually split their computing tasks. In this paper, we present a suite of new techniques that make such privacy-aware data-intensive computing possible. Our system, called Sedic, leverages the special features of MapReduce to automatically partition a computing job according to the security levels of the data it works on, and arrange the computation across a hybrid cloud. Specifically, we modified MapReduce's distributed file system to strategically replicate data, moving sanitized data blocks to the public cloud. Over this data placement, map tasks are carefully scheduled to outsource as much workload to the public cloud as possible, given sensitive data always stay on the private cloud. To minimize inter-cloud communication, our approach also automatically analyzes and transforms the reduction structure of a submitted job to aggregate the map outcomes within the public cloud before sending the result back to the private cloud for the final reduction. This also allows the users to interact with our system in the same way they work with MapReduce, and directly run their legacy code in our framework. We implemented Sedic on Hadoop and evaluated it using both real and synthesized computing jobs on a large-scale cloud test-bed. The study shows that our techniques effectively protect sensitive user data, offload a large amount of computation to the public cloud and also fully preserve the scalability of MapReduce.
211 citations
TL;DR: In this article, the privacy and consumer risks that are associated with cloud computing are examined.
211 citations
TL;DR: This work defines a privacy measure in terms of information theory, similar to t-closeness, and uses the tools of that theory to show that this privacy measure can be achieved by the postrandomization method (PRAM) for masking in the discrete case, and by a form of noise addition in the general case.
Abstract: t-Closeness is a privacy model recently defined for data anonymization. A data set is said to satisfy t-closeness if, for each group of records sharing a combination of key attributes, the distance between the distribution of a confidential attribute in the group and the distribution of the attribute in the entire data set is no more than a threshold t. Here, we define a privacy measure in terms of information theory, similar to t-closeness. Then, we use the tools of that theory to show that our privacy measure can be achieved by the postrandomization method (PRAM) for masking in the discrete case, and by a form of noise addition in the general case.
210 citations
TL;DR: The results showed that individual differences, nationality and national culture significantly influenced internet users' privacy concerns to the extent that older, female internet users from an individualistic culture were more concerned about online privacy than their counterparts.
Abstract: This study surveyed 1261 internet users from five cities (Bangalore, Seoul, Singapore, Sydney and New York) to examine multinational internet users' perceptions and behavioural responses concerning online privacy. It identified a set of individual-level (demographics and internet-related experiences) and macro-level factors (nationality and national culture), and tested the extent to which they affected online privacy concerns and privacy protection behaviours. The results showed that individual differences (age, gender and internet experience), nationality and national culture significantly influenced internet users' privacy concerns to the extent that older, female internet users from an individualistic culture were more concerned about online privacy than their counterparts. The study also identified three underlying dimensions of privacy protection behaviour — avoidance, opt-out and proactive protection — and found that they distinctly related to the individual and macro-level factors. Overall, the fi...
210 citations