38,181 resources related to Privacy
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ICC 2021 - IEEE International Conference on Communications
IEEE ICC is one of the two flagship IEEE conferences in the field of communications; Montreal is to host this conference in 2021. Each annual IEEE ICC conference typically attracts approximately 1,500-2,000 attendees, and will present over 1,000 research works over its duration. As well as being an opportunity to share pioneering research ideas and developments, the conference is also an excellent networking and publicity event, giving the opportunity for businesses and clients to link together, and presenting the scope for companies to publicize themselves and their products among the leaders of communications industries from all over the world.
Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.
The Eleventh International Conference on Quality of Multimedia Experience will bring together leading experts from academia and industry to present and discuss current and future research on multimedia quality, quality of experience (QoE) and user experience (UX). This way, it will contribute towards an integrated view on QoE and UX, and foster the exchange between the so-far distinct communities.
The convergence of current technologies provides the infrastructure for transmitting and storing information faster and cheaper. For information to be used in gaining knowledge, however, environments for collecting, storing, disseminating, sharing and constructing knowledge are needed. Such environments, knowledge media, brings together telecommunication, computer and networking technologies, learning theories and cognitive sciences to form meaningful environments that provides for a variety of learner needs. ITHET 2018 will continue with the traditional themes of previous events. However, our special theme for this year is a fundamental one. We have previously had MOOCs as our special theme, but now they are just infrastructure. Even “Blended Learning” is what we all do anyway. In a time of the unprecedented access to knowledge through IT, it is time for us to revisit the fundamental purpose of our educational system. It is certainly not about knowledge anymore.
The 17th International Conference on Computer and Information Science (ICIS 2018) bringstogether scientists, engineers, computer users and students to exchange and share theirexperiences, new ideas and research results about all aspects (theory, applications and tools)of computer and information science and discuss the practical challenges encountered and thesolutions adopted.
Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...
IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...
Each tutorial reviews currents communications topics in network management and computer and wireless communications. Available tutorials, which are 2.5 to 5 hours in length contains the original visuals and voice-over by the presenter. IEEE Communications Surveys & Tutorials features two distinct types of articles: original articles and reprints. The original articles are exclusively written for IEEE Communications Surveys & Tutorials ...
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
Rigorously peer-reviewed forum for publishing early, high-impact results in the areas of uni- and multiprocessors computer systems, computer architecture workload characterization, performance evaluation and simulation techniques, and power-aware computing
2018 IEEE Symposium on Privacy-Aware Computing (PAC), 2018
Research shows that Facebook users differ extensively in their use of various privacy features, and that they generally find it difficult to translate their desired privacy preferences into concrete interface actions. Our work explores the use of User-Tailored Privacy (UTP) to adapt Facebook's privacy features to the user's personal preferences. We developed adaptive versions of 19 Facebook privacy features, and ...
2018 16th Annual Conference on Privacy, Security and Trust (PST), 2018
In smart homes, users do not have enough options to express their privacy preferences and decide who can see their data, when their data can be used, and which part of data they want to share and which part they do not want. We propose a framework that uses machine learning algorithms to determine for what purpose, to whom and ...
2015 IEEE Security and Privacy Workshops, 2015
Data protection authorities worldwide have agreed on the value of considering privacy-by-design principles when developing privacy-friendly systems and software. However, on the technical plane, a profusion of privacy-oriented guidelines and approaches coexists, which provides partial solutions to the overall problem and aids engineers during different stages of the system development lifecycle. As a result, engineers find difficult to understand what ...
IEEE Transactions on Information Forensics and Security, 2017
Differential privacy is a popular privacy model within the research community because of the strong privacy guarantee it offers, namely that the presence or absence of any individual in a data set does not significantly influence the results of analyses on the data set. However, enforcing this strict guarantee in practice significantly distorts data and/or limits data uses, thus diminishing ...
IEEE Security & Privacy, 2014
Although the focus here on teaching privacy to computer scientists, the author wants to first mention the law school approach, which is, of course, lawyerly. The topics in a typical law school course on information privacy include the development of privacy within the law; privacy law in commercial practice, health information, and communications; privacy and data protection, including the international ...
Luca Bolognini: Internet of Things: Privacy and Security Challenges - WF-IoT 2015
Tutorial 2: 5G Security & Privacy - NetSoft 2020 Conference
IEEE Future Directions: What is the Internet of Things?
Big Data & the Cloud: Privacy and Security issues
Antonio Skarmeta: Security and Privacy in the Internet of Things - WF-IoT 2015
Panelist: Chaim Cohen - ETAP Delhi 2016
Security and Privacy in a World of Connected Devices
IEEE Summit on Internet Governance 2014: Panel II - Security vs. Privacy
Surround Sound Headphones For Realistic Gaming
IEEE World Forum on Internet of Things - Milan, Italy - Sara Foresti - Data Security and Privacy in the Internet of Things - Part 1
Privacy, security, and innovation challenges in different aspects of IoT - Panel from ETAP Forum, February 2016
Glenn Fink on Priorities for IoT Security and Privacy From Here to 2020: End to End Trust and Security Workshop for the Internet of Things 2016
Antonio Skarmeta: IoT Security and Privacy - Industry Forum Panel Introduction: WF IoT 2016
Adventures in Usable Privacy and Security: From Empirical Studies to Public Policy - IEEE SecDev 2016
IEEE World Forum on Internet of Things - Milan, Italy - Sara Foresti - Data Security and Privacy in the Internet of Things - Part 3
Privacy Consideration in the New Digital Era - Fog World Congress 2017
Keynote Isaac Ben-Israel - ETAP Forum Tel Aviv 2016
Speaker Jessica Groopman - ETAP San Jose 2015
Breakout Session Report-Outs - ETAP Tel Aviv 2015
Research shows that Facebook users differ extensively in their use of various privacy features, and that they generally find it difficult to translate their desired privacy preferences into concrete interface actions. Our work explores the use of User-Tailored Privacy (UTP) to adapt Facebook's privacy features to the user's personal preferences. We developed adaptive versions of 19 Facebook privacy features, and for each feature we test three adaptation methods (Automation, Highlight and Suggestion) that can be used to implement the adaptive behavior. In a "think-aloud" semistructured interview study (N=18), we show participants paper prototypes of our adaptive privacy features and ask participants to judge the presented adaptive capabilities and the three adaptation methods that implement them. Our findings provide insights into the viability of User-Tailored Privacy. Specifically, we find that the optimal adaptation method depends on the users' familiarity with the privacy feature and how they use them, and their judgment of the awkwardness and irreversibility of the implemented privacy functionality. We conclude with design recommendations for the implementation of User-Tailored Privacy on Facebook and other social network platforms.
In smart homes, users do not have enough options to express their privacy preferences and decide who can see their data, when their data can be used, and which part of data they want to share and which part they do not want. We propose a framework that uses machine learning algorithms to determine for what purpose, to whom and with what level of details the information will be shared.
Data protection authorities worldwide have agreed on the value of considering privacy-by-design principles when developing privacy-friendly systems and software. However, on the technical plane, a profusion of privacy-oriented guidelines and approaches coexists, which provides partial solutions to the overall problem and aids engineers during different stages of the system development lifecycle. As a result, engineers find difficult to understand what they should do to make their systems abide by privacy by design, thus hindering the adoption of privacy engineering practices. This paper reviews existing best practices in the analysis and design stages of the system development lifecycle, introduces a systematic methodology for privacy engineering that merges and integrates them, leveraging their best features whilst addressing their weak points, and describes its alignment with current standardization efforts.
Differential privacy is a popular privacy model within the research community because of the strong privacy guarantee it offers, namely that the presence or absence of any individual in a data set does not significantly influence the results of analyses on the data set. However, enforcing this strict guarantee in practice significantly distorts data and/or limits data uses, thus diminishing the analytical utility of the differentially private results. In an attempt to address this shortcoming, several relaxations of differential privacy have been proposed that trade off privacy guarantees for improved data utility. In this paper, we argue that the standard formalization of differential privacy is stricter than required by the intuitive privacy guarantee it seeks. In particular, the standard formalization requires indistinguishability of results between any pair of neighbor data sets, while indistinguishability between the actual data set and its neighbor data sets should be enough. This limits the data controller's ability to adjust the level of protection to the actual data, hence resulting in significant accuracy loss. In this respect, we propose individual differential privacy, an alternative differential privacy notion that offers the same privacy guarantees as standard differential privacy to individuals (even though not to groups of individuals). This new notion allows the data controller to adjust the distortion to the actual data set, which results in less distortion and more analytical accuracy. We propose several mechanisms to attain individual differential privacy and we compare the new notion against standard differential privacy in terms of the accuracy of the analytical results.
Although the focus here on teaching privacy to computer scientists, the author wants to first mention the law school approach, which is, of course, lawyerly. The topics in a typical law school course on information privacy include the development of privacy within the law; privacy law in commercial practice, health information, and communications; privacy and data protection, including the international aspects of this; and regulatory frameworks for privacy. In rare cases, mostly those in which the faculty member does cyberlaw research, the course might cover technological protections for privacy. Undergraduate and graduate computer science courses in privacy have different audiences and different goals from law school ones; they also differ from each other. An undergraduate course should present myriad privacy approaches, whereas a graduate course might well focus on current technological research.
How can we help users balance the benefits and risks of information disclosure online? A user-tailored privacy approach makes privacy decisions less burdensome by giving users the right kind of information and the right amount of control so as to be useful but not overwhelming or misleading.
It might be possible for individual actors in a marketplace to drive the adoption of particular privacy and security standards. Using HTTPS, two-factor authentication, and end-to-end encryption as case studies, the author tries to ascertain which factors are responsible for successful diffusion that improves the privacy of a large number of users.
Smartphone app developers make many privacy-related decisions on what data to collect about users and how that data is used. Based on interviews and a survey of app developers, the authors identify several hurdles preventing app developers from improved privacy behaviors. These include the difficulties of reading and writing privacy policies as well as privacy not being their primary task. The authors suggest some nudges that would help app developers improve user privacy as well as public policy focus on incentivizing all players in the app development ecosystem to help developers implement better privacy behaviors.
The increasing publication of large amounts of data, theoretically anonymous, can lead to a number of attacks on the privacy of people. The publication of sensitive data without exposing the data owners is generally not part of the software developers concerns. The regulations for the data privacy-preserving create an appropriate scenario to focus on privacy from the perspective of the use or data exploration that takes place in an organization. The increasing number of sanctions for privacy violations motivates the systematic comparison of three known machine learning algorithms in order to measure the usefulness of the data privacy preserving. The scope of the evaluation is extended by comparing them with a known privacy preservation metric. Different parameter scenarios and privacy levels are used. The use of publicly available implementations, the presentation of the methodology, explanation of the experiments and the analysis allow providing a framework of work on the problem of the preservation of privacy. Problems are shown in the measurement of the usefulness of the data and its relationship with the privacy preserving. The findings motivate the need to create optimized metrics on the privacy preferences of the owners of the data since the risks of predicting sensitive attributes by means of machine learning techniques are not usually eliminated. In addition, it is shown that there may be a hundred percent, but it cannot be measured. As well as ensuring adequate performance of machine learning models that are of interest to the organization that data publisher.
Research, compile, and consolidate information leading to the publication of a standard for exchanging DSRC information, providing for bi-directional message transmission and device control, in a manner which is compatible with but independent of the ASTM efforts to specify DSRC Layers 1 and 2. This will entail specifying the transponder resources, the transponder resource manager, the application message sets, and ...