Privacy, Anonymity, and Big Data in the Social Sciences

Quality social science research and the privacy of human subjects require trust

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

Knowing Your Population: Privacy-Sensitive Mining of Massive Data

Location and mobility patterns of individuals are important to environmental planning, societal resilience, public health, and a host of commercial applications. Mining telecommunication traffic and transactions data for such purposes is controversial, in particular raising issues of privacy. However, our hypothesis is that privacy-sensitive uses are possible and often beneficial enough to warrant considerable research and development efforts. Our work contends that peoples behavior can yield patterns of both significant commercial, and research, value. For such purposes, methods and algorithms for mining telecommunication data to extract commonly used routes and locations, articulated through time-geographical constructs, are described in a case study within the area of transportation planning and analysis. From the outset, these were designed to balance the privacy of subscribers and the added value of mobility patterns derived from their mobile communication traffic and transactions data. Our work directly contrasts the current, commonly held notion that value can only be added to services by directly monitoring the behavior of individuals, such as in current attempts at location-based services. We position our work within relevant legal frameworks for privacy and data protection, and show that our methods comply with such requirements and also follow best-practice

"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

Big Data Privacy Context: Literature Effects On Secure Informational Assets

This article's objective is the identification of research opportunities in the current big data privacy domain, evaluating literature effects on secure informational assets. Until now, no study has analyzed such relation. Its results can foster science, technologies and businesses. To achieve these objectives, a big data privacy Systematic Literature Review (SLR) is performed on the main scientific peer reviewed journals in Scopus database. Bibliometrics and text mining analysis complement the SLR. This study provides support to big data privacy researchers on: most and least researched themes, research novelty, most cited works and authors, themes evolution through time and many others. In addition, TOPSIS and VIKOR ranks were developed to evaluate literature effects versus informational assets indicators. Secure Internet Servers (SIS) was chosen as decision criteria. Results show that big data privacy literature is strongly focused on computational aspects. However, individuals, societies, organizations and governments face a technological change that has just started to be investigated, with growing concerns on law and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions and the only consistent country between literature and SIS adoption is the United States. Countries in the lowest ranking positions represent future research opportunities.Comment: 21 pages, 9 figure

Privacy in Public and the contextual conditions of agency

Current technology and surveillance practices make behaviors traceable to persons in unprecedented ways. This causes a loss of anonymity and of many privacy measures relied on in the past. These de facto privacy losses are by many seen as problematic for individual psychology, intimate relations and democratic practices such as free speech and free assembly. I share most of these concerns but propose that an even more fundamental problem might be that our very ability to act as autonomous and purposive agents relies on some degree of privacy, perhaps particularly as we act in public and semi-public spaces. I suggest that basic issues concerning action choices have been left largely unexplored, due to a series of problematic theoretical assumptions at the heart of privacy debates. One such assumption has to do with the influential conceptualization of privacy as pertaining to personal intimate facts belonging to a private sphere as opposed to a public sphere of public facts. As Helen Nissenbaum has pointed out, the notion of privacy in public sounds almost like an oxymoron given this traditional private-public dichotomy. I discuss her important attempt to defend privacy in public through her concept of ‘contextual integrity.’ Context is crucial, but Nissenbaum’s descriptive notion of existing norms seems to fall short of a solution. I here agree with Joel Reidenberg’s recent worries regarding any approach that relies on ‘reasonable expectations’ . The problem is that in many current contexts we have no such expectations. Our contexts have already lost their integrity, so to speak. By way of a functional and more biologically inspired account, I analyze the relational and contextual dynamics of both privacy needs and harms. Through an understanding of action choice as situated and options and capabilities as relational, a more consequence-oriented notion of privacy begins to appear. I suggest that privacy needs, harms and protections are relational. Privacy might have less to do with seclusion and absolute transactional control than hitherto thought. It might instead hinge on capacities to limit the social consequences of our actions through knowing and shaping our perceptible agency and social contexts of action. To act with intent we generally need the ability to conceal during exposure. If this analysis is correct then relational privacy is an important condition for autonomic purposive and responsible agency—particularly in public space. Overall, this chapter offers a first stab at a reconceptualization of our privacy needs as relational to contexts of action. In terms of ‘rights to privacy’ this means that we should expand our view from the regulation and protection of the information of individuals to questions of the kind of contexts we are creating. I am here particularly interested in what I call ‘unbounded contexts’, i.e. cases of context collapses, hidden audiences and even unknowable future agents