Fanny Coudert

Bio: Fanny Coudert is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Data Protection Act 1998 & Privacy by Design. The author has an hindex of 7, co-authored 24 publications receiving 204 citations.

In The Aftermath of the Promusicae Case: How to Strike the Balance?

Abstract: Copyright societies are currently pushing for increased private enforcement of intellectual property rights on the Internet, in particular by trying to involve Internet Service Providers (ISPs) in their combat against copyright infringements, and by pushing for new legislative mechanisms. This raises serious legal problems and questions both in terms of the protection of users’ privacy, their right to a fair trial, and the liability of ISPs. This article discusses the difficult task of balancing copyright interests and fundamental rights as debated in the Promusicae case.

Privacy by Design: From Research and Policy to Practice – the Challenge of Multi-disciplinarity

Abstract: The concept of Privacy by Design (PbD) is a vision for creating data-processing environments in a way that respects privacy and data protection in the design of products and processes from the start. PbD has been inspired by and elaborated in different disciplines (especially law and computer science). Developments have taken place in research and policy, with the General Data Protection Regulation to be adopted by the European Parliament in 2016 and to enter into force in 2018. It is now time to use the results for practical guidance on how to achieve the goals defined by the legislation. In this paper, we summarise lessons learned from the special session on Multidisciplinary Aspects of PbD organised at the Annual Privacy Forum 2015. In particular, we identify important current and future implementation challenges of PbD. These are: terminology, legal compliance, different disciplines’ understandings, the role of the data protection officer, the involvement of all stakeholders, and education. We conclude by emphasising the importance of approaching PbD in an interdisciplinary way.

Privacy Settings in Social Networking Sites: Is It Fair?

Abstract: The present paper examines privacy settings in Social Networking Sites (SNS) and their default state from the legal point of view. The analysis will be conducted on the example of Facebook as one of the most popular –and controversial- SNS and one of the most active providers constantly amending its privacy settings. The paper will first present the notion of privacy settings and will explain how they can contribute to protecting the privacy of the user. Further on, this paper will discuss the general concerns expressed by users and data protection authorities worldwide with regard to the changes of Facebook’s privacy settings introduced in February 2010. Focus will be put on the implementation of the fairness principle in SNS. This principle implies that a person is not unduly pressured into supplying his data to a data controller, and on the other hand that the processing of personal data is transparent for the data subject.

Visual privacy protection methods

Abstract: This work is the first comprehensive review about visual privacy protection.Several categories are established for all of the available privacy protection methods.A review of intelligent monitoring systems that takes privacy into account is provided.An overall vision of the state of the art is given to introduce novel researchers.A critical discussion about relevant key topics and open issues is provided. Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambient-assisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual's privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users' acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people's privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.

Enhancing Lifelogging Privacy by Detecting Screens

Abstract: Low-cost, lightweight wearable cameras let us record (or 'lifelog') our lives from a 'first-person' perspective for purposes ranging from fun to therapy. But they also capture private information that people may not want to be recorded, especially if images are stored in the cloud or visible to other people. For example, recent studies suggest that computer screens may be lifeloggers' single greatest privacy concern, because many people spend a considerable amount of time in front of devices that display private information. In this paper, we investigate using computer vision to automatically detect computer screens in photo lifelogs. We evaluate our approach on an existing in-situ dataset of 36 people who wore cameras for a week, and show that our technique could help manage privacy in the upcoming era of wearable cameras.

Mapping fatal police violence across U.S. metropolitan areas: Overall rates and racial/ethnic inequities, 2013-2017

Abstract: Background & methods Recent social movements have highlighted fatal police violence as an enduring public health problem in the United States To solve it, the public requires basic information, such as understanding where rates of fatal police violence are particularly high, and for which groups Existing mapping efforts, though critically important, often use inappropriate statistical methods and can produce misleading, unstable rates when denominators are small To fill this gap, we use inverse-variance-weighted multilevel models to estimate overall and race-stratified rates of fatal police violence for all Metropolitan Statistical Areas (MSAs) in the US (2013–2017), as well as racial inequities in these rates We analyzed the most recent, reliable data from Fatal Encounters, a citizen science initiative that aggregates and verifies media reports Results Rates of police-related fatalities varied dramatically, with the deadliest MSAs exhibiting rates nine times those of the least deadly Overall rates in Southwestern MSAs were highest, with lower rates in the northern Midwest and Northeast Yet this pattern was reversed for Black-White inequities, with Northeast and Midwest MSAs exhibiting the highest inequities nationwide Our main results excluded deaths that could be considered accidents (eg, vehicular collisions), but sensitivity analyses demonstrated that doing so may underestimate the rate of fatal police violence in some MSAs by 60% Black-White and Latinx-White inequities were slightly underestimated nationally by excluding reportedly ‘accidental’ deaths, but MSA-specific inequities were sometimes severely under- or over-estimated Conclusions Preventing fatal police violence in different areas of the country will likely require unique solutions Estimates of the severity of these problems (overall rates, racial inequities, specific causes of death) in any given MSA are quite sensitive to which types of deaths are analyzed, and whether race and cause of death are attributed correctly Monitoring and mapping these rates using appropriate methods is critical for government accountability and successful prevention