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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
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Proceedings ArticleDOI
19 May 2013
TL;DR: The design and implementation of DARKLY is described, a practical privacy protection system for the increasingly common scenario where an untrusted, third-party perceptual application is running on a trusted device and it is demonstrated that utility remains acceptable even with strong privacy protection.
Abstract: Perceptual, "context-aware" applications that observe their environment and interact with users via cameras and other sensors are becoming ubiquitous on personal computers, mobile phones, gaming platforms, household robots, and augmented-reality devices. This raises new privacy risks. We describe the design and implementation of DARKLY, a practical privacy protection system for the increasingly common scenario where an untrusted, third-party perceptual application is running on a trusted device. DARKLY is integrated with OpenCV, a popular computer vision library used by such applications to access visual inputs. It deploys multiple privacy protection mechanisms, including access control, algorithmic privacy transforms, and user audit. We evaluate DARKLY on 20 perceptual applications that perform diverse tasks such as image recognition, object tracking, security surveillance, and face detection. These applications run on DARKLY unmodified or with very few modifications and minimal performance overheads vs. native OpenCV. In most cases, privacy enforcement does not reduce the applications' functionality or accuracy. For the rest, we quantify the tradeoff between privacy and utility and demonstrate that utility remains acceptable even with strong privacy protection.

148 citations

Journal ArticleDOI
Qian Wang1, Yan Zhang1, Xiao Lu1, Zhibo Wang1, Zhan Qin2, Kui Ren2 
TL;DR: Experimental results show that the proposed schemes outperform the existing methods and improve the utility of real-time data sharing with strong privacy guarantee.
Abstract: Nowadays gigantic crowd-sourced data from mobile devices have become widely available in social networks, enabling the possibility of many important data mining applications to improve the quality of our daily lives. While providing tremendous benefits, the release of crowd-sourced social network data to the public will pose considerable threats to mobile users’ privacy. In this paper, we investigate the problem of real-time spatio-temporal data publishing in social networks with privacy preservation. Specifically, we consider continuous publication of population statistics and design RescueDP—an online aggregate monitoring framework over infinite streams with $w$ -event privacy guarantee. Its key components including adaptive sampling, adaptive budget allocation, dynamic grouping, perturbation and filtering, are seamlessly integrated as a whole to provide privacy-preserving statistics publishing on infinite time stamps. Moreover, we further propose an enhanced RescueDP with neural networks to accurately predict the values of statistics and improve the utility of released data. Both RescueDP and the enhanced RescueDP are proved satisfying $w$ -event privacy. We evaluate the proposed schemes with real-world as well as synthetic datasets and compare them with two $w$ -event privacy-assured representative methods. Experimental results show that the proposed schemes outperform the existing methods and improve the utility of real-time data sharing with strong privacy guarantee.

148 citations

Journal ArticleDOI
Wei Zhang1, Yaping Lin1, Sheng Xiao1, Jie Wu2, Siwang Zhou1 
TL;DR: To enable cloud servers to perform secure search without knowing the actual data of both keywords and trapdoors, a novel secure search protocol is systematically constructed and a novel additive order and privacy preserving function family is proposed.
Abstract: With the advent of cloud computing, it has become increasingly popular for data owners to outsource their data to public cloud servers while allowing data users to retrieve this data. For privacy concerns, secure searches over encrypted cloud data has motivated several research works under the single owner model. However, most cloud servers in practice do not just serve one owner; instead, they support multiple owners to share the benefits brought by cloud computing. In this paper, we propose schemes to deal with privacy preserving ranked multi-keyword search in a multi-owner model (PRMSM). To enable cloud servers to perform secure search without knowing the actual data of both keywords and trapdoors, we systematically construct a novel secure search protocol. To rank the search results and preserve the privacy of relevance scores between keywords and files, we propose a novel additive order and privacy preserving function family. To prevent the attackers from eavesdropping secret keys and pretending to be legal data users submitting searches, we propose a novel dynamic secret key generation protocol and a new data user authentication protocol. Furthermore, PRMSM supports efficient data user revocation. Extensive experiments on real-world datasets confirm the efficacy and efficiency of PRMSM.

148 citations

Proceedings ArticleDOI
06 May 1996
TL;DR: This model was commissioned by doctors and is driven by medical ethics; it is informed by the actual threats to privacy, and reflects current best clinical practice, and its effect is to restrict both the number of users who can access any record and the maximum number of records accessed by any user.
Abstract: The protection of personal health information has become a live issue in a number of countries, including the USA, Canada, Britain and Germany. The debate has shown that there is widespread confusion about what should be protected, and why. Designers of military and banking systems can refer to Bell & LaPadula (1973) and Clark & Wilson (1987) respectively, but there is no comparable security policy model that spells out clear and concise access rules for clinical information systems. In this article, we present just such a model. It was commissioned by doctors and is driven by medical ethics; it is informed by the actual threats to privacy, and reflects current best clinical practice. Its effect is to restrict both the number of users who can access any record and the maximum number of records accessed by any user. This entails controlling information flows across rather than down and enforcing a strong notification property. We discuss its relationship with existing security policy models, and its possible use in other applications where information exposure must be localised; these range from private banking to the management of intelligence data.

148 citations

Proceedings ArticleDOI
01 Sep 2018
TL;DR: This research work proposes a conceptual design for sharing personal continuous-dynamic health data using blockchain technology supplemented by cloud storage to share the health-related information in a secure and transparent manner and introduces a data quality inspection module based on machine learning techniques to have control over data quality.
Abstract: With the advent of rapid development of wearable technology and mobile computing, huge amount of personal health-related data is being generated and accumulated on continuous basis at every moment. These personal datasets contain valuable information and they belong to and asset of the individual users, hence should be owned and controlled by themselves. Currently most of such datasets are stored and controlled by different service providers and this centralised data storage brings challenges of data security and hinders the data sharing. These personal health data are valuable resources for healthcare research and commercial projects. In this research work, we propose a conceptual design for sharing personal continuous-dynamic health data using blockchain technology supplemented by cloud storage to share the health-related information in a secure and transparent manner. Besides, we also introduce a data quality inspection module based on machine learning techniques to have control over data quality. The primary goal of the proposed system is to enable users to own, control and share their personal health data securely, in a General Data Protection Regulation (GDPR) compliant way to get benefit from their personal datasets. It also provides an efficient way for researchers and commercial data consumers to collect high quality personal health data for research and commercial purposes.

148 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023562
20221,226
20211,535
20201,634
20191,255
20181,277