Facial Recognition Technology A Survey of Policy and Implementation Issues

Facial recognition technology (FRT) has emerged as an attractive solution to address many contemporary needs for identification and the verification of identity claims. As FRT increasingly moves from the research laboratory into the world of socio-political concerns and practices there is a need to bridge the divide between a purely technical and a purely socio-political analysis of FRT. This is the aim of this report. In doing this the report addresses the unique challenges and concerns that attend its development, evaluation, and specific operational uses, contexts, and goals. It highlights the potential and limitations of the technology, noting those tasks for which it seems ready for deployment, those areas where performance obstacles may be overcome by future technological developments or sound operating procedures, and still other issues which appear intractable. As such its concern with efficacy also extends to ethical considerations

In Things We Trust? Towards trustability in the Internet of Things

This essay discusses the main privacy, security and trustability issues with the Internet of Things

"The dearest of our possessions": applying Floridi's information privacy concept in models of information behavior and information literacy

This conceptual paper argues for the value of an approach to privacy in the digital information environment informed by Luciano Floridi's philosophy of information and information ethics. This approach involves achieving informational privacy, through the features of anonymity and obscurity, through an optimal balance of ontological frictions. This approach may be used to modify models for information behavior and for information literacy, giving them a fuller and more effective coverage of privacy issues in the infosphere. For information behavior, the Information Seeking and Communication Model, and the Information Grounds conception, are most appropriate for this purpose. For information literacy, the metaliteracy model, using a modification a privacy literacy framework, is most suitable

Sensible Privacy: How We Can Protect Domestic Violence Survivors Without Facilitating Misuse

Privacy is a concept with real life ties and implications. Privacy infringement has the potential to lead to serious consequences for the stakeholders involved, hence researchers and organisations have developed various privacy enhancing techniques and tools. However, there is no solution that fits all, and there are instances where privacy solutions could be misused, for example to hide nefarious activities. Therefore, it is important to provide suitable measures and to make necessary design tradeoffs in order to avoid such misuse. This short paper aims to make a case for the need of careful consideration when designing a privacy solution, such that the design effectively addresses the user requirements while at the same time minimises the risk of inadvertently assisting potential offenders. In other words, this paper strives to promote “sensible privacy” design, which deals with the complex challenges in balancing privacy, usability and accountability. We illustrate this idea through a case study involving the design of privacy solutions for domestic violence survivors. This is the main contribution of the paper. The case study presents specific user requirements and operating conditions, which coupled with the attacker model, provide a complex yet interesting scenario to explore. One example of our solutions is described in detail to demonstrate the feasibility of our approach

Big data, method and the ethics of location : a case study of a hookup app for men who have sex with men

With the rise of geo-social media, location is emerging as a particularly sensitive data point for big data and digital media research. To explore this area, we reflect on our ethics for a study in which we analyze data generated via an app that facilitates public sex among men who have sex with men. The ethical sensitivities around location are further heightened in the context of research into such digital sexual cultures. Public sexual cultures involving men who have sex with men operate both in spaces “meant” for public sex (e.g., gay saunas and dark rooms) and spaces “not meant” for public sex (e.g., shopping centers and public toilets). The app in question facilitates this activity. We developed a web scraper that carefully collected selected data from the app and that data were then analyzed to help identify ethical issues. We used a mixture of content analysis using Python scripts, geovisualisation software and manual qualitative coding techniques. Our findings, which are methodological rather than theoretical in nature, center on the ethics associated with generating, processing, presenting, archiving and deleting big data in a context where harassment, imprisonment, physical harm and even death occur. We find a tension in normal standards of ethical conduct where humans are involved in research. We found that location came to the fore as a key - though not the only - actor requiring attention when considering ethics in a big data context

A qualitative study of stakeholders' perspectives on the social network service environment

Over two billion people are using the Internet at present, assisted by the mediating activities of software agents which deal with the diversity and complexity of information. There are, however, ethical issues due to the monitoring-and-surveillance, data mining and autonomous nature of software agents. Considering the context, this study aims to comprehend stakeholders' perspectives on the social network service environment in order to identify the main considerations for the design of software agents in social network services in the near future. Twenty-one stakeholders, belonging to three key stakeholder groups, were recruited using a purposive sampling strategy for unstandardised semi-structured e-mail interviews. The interview data were analysed using a qualitative content analysis method. It was possible to identify three main considerations for the design of software agents in social network services, which were classified into the following categories: comprehensive understanding of users' perception of privacy, user type recognition algorithms for software agent development and existing software agents enhancement

C3P: Context-Aware Crowdsourced Cloud Privacy

Due to the abundance of attractive services available on the cloud, people are placing an increasing amount of their data online on different cloud platforms. However, given the recent large-scale attacks on users data, privacy has become an important issue. Ordinary users cannot be expected to manually specify which of their data is sensitive or to take appropriate measures to protect such data. Furthermore, usually most people are not aware of the privacy risk that different shared data items can pose. In this paper, we present a novel conceptual framework in which privacy risk is automatically calculated using the sharing context of data items. To overcome ignorance of privacy risk on the part of most users, we use a crowdsourcing based approach. We use Item Response Theory (IRT) on top of this crowdsourced data to determine privacy risk of items and diverse attitudes of users towards privacy. First, we determine the feasibility of IRT for the cloud scenario by asking workers feedback on Amazon mTurk on various sharing scenarios. We obtain a good fit of the responses with the theory, and thus show that IRT, a well-known psychometric model for educational purposes, can be applied to the cloud scenario. Then, we present a lightweight mechanism such that users can crowdsource their sharing contexts with the server and obtain the risk of sharing particular data item(s) anonymously. Finally, we use the Enron dataset to simulate our conceptual framework, and also provide experimental results using synthetic data. We show that our scheme converges quickly and provides accurate privacy risk scores under varying conditions

Market research & the ethics of big data

The term ‘big data’ has recently emerged to describe a range of technological and commercial trends enabling the storage and analysis of huge amounts of customer data, such as that generated by social networks and mobile devices. Much of the commercial promise of big data is in the ability to generate valuable insights from collecting new types and volumes of data in ways that were not previously economically viable. At the same time a number of questions have been raised about the implications for individual privacy. This paper explores key perspectives underlying the emergence of big data and considers both the opportunities and ethical challenges raised for market research