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Focus group methodology generates distinct ethical challenges that do not correspond fully to those raised by one-to-one interviews. This paper explores, in both conceptual and practical terms, three key issues: consent; confidentiality and anonymity; and risk of harm. The principal challenge in obtaining consent lies in giving a clear account of what will take place in the group, owing to unpredictability of the discussion and interaction that will occur. As consent can be seen in terms of creating appropriate expectations in the participant, this may therefore be hard to achieve. Moreover, it is less straightforward for the participant to revoke consent than in one-to-one interviews. Confidentiality and anonymity are potentially problematic because of the researcher’s limited control over what participants may subsequently communicate outside the group. If the group discussion encourages over-disclosure by some participants, this problem becomes more acute. Harm in a focus group may arise from the discussion of sensitive topics, and this may be amplified by the public nature of the discussion. A balance should be struck between avoiding or closing down potentially distressing discussion and silencing the voices of certain participants to whom such discussion may be important or beneficial. As a means of addressing the above issues, we outline some strategies that can be adopted in the consent process, in a preliminary briefing session, during moderation of the focus group, and in a subsequent debriefing, and suggest that these strategies can be employed synergistically so as to reinforce each other.

Recent years have seen the wide application of Location-Based Services (LBSs) in our daily life. Although users can enjoy many conveniences from the LBSs, they may lose their trajectory privacy when their location data are collected. Therefore, it is urgent to protect the user’s trajectory privacy while providing high quality services. Trajectory k-anonymity is one of the most important technologies to protect the user’s trajectory privacy. However, the user’s attributes are rarely considered when constructing the k-anonymity set. It results in that the user’s trajectories are especially vulnerable. To solve the problem, in this paper, a Spatiotemporal Mobility (SM) measurement is defined for calculating the relationship between the user’s attributes and the anonymity set. Furthermore, a trajectory graph is designed to model the relationship between trajectories. Based on the user’s attributes and the trajectory graph, the SM based trajectory privacy-preserving algorithm (MTPPA) is proposed. The optimal k-anonymity set is obtained by the simulated annealing algorithm. The experimental results show that the privacy disclosure probability of the anonymity set obtained by MTPPA is about 40% lower than those obtained by the existing algorithms while the same quality of services can be provided.

The rise of social media has fostered increasing instances of deviant behavior. Arguably, the most notable of these is cyberbullying (CB), which is an increasing global concern because of the social and financial ramifications. This has necessitated a new line of research aimed at understanding and preventing CB. Although much progress has been made in understanding CB, little is known about how to prevent it, especially through the information technology (IT) design. Based on the need for a better causal theory and more effective empirical methods to investigate and mitigate this phenomenon, we leverage the control balance theory (CBT) for system design. Our model examines the causes of CB from several novel angles, including (1) the strong nonlinear influence of control imbalances on CB, and (2) using the concept of fit to understand how different design features of information technology artifacts influence factors such as deindividuation and accountability, thus affecting control imbalance. Using an innovative factorial survey method that enabled us to manipulate IT design features to obtain a nuanced view, we tested our model with 507 adults and found strong support for our model. The results show that IT design features create a strong CB opportunity for individuals who perceive that they are controlled by others. Whether this perception is real or imagined, it creates a sense of vulnerability, prompting them to engage in CB. We can thus propose specific IT design feature manipulations that can be used to discourage CB. These results should have salient implications for researchers and social media designers, especially in developing social media networks that are safe, supportive, responsible, and constructive.

Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results. Big data analytics is the term used to describe the process of researching massive amounts of complex data in order to reveal hidden patterns or identify secret correlations. However, there is an obvious contradiction between the security and privacy of big data and the widespread use of big data. This paper focuses on privacy and security concerns in big data, differentiates between privacy and security and privacy requirements in big data. This paper covers uses of privacy by taking existing methods such as HybrEx, k-anonymity, T-closeness and L-diversity and its implementation in business. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (for example, data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a major review of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, identity based anonymization, differential privacy, privacy-preserving big data publishing and fast anonymization of big data streams. This paper refer privacy and security aspects healthcare in big data. Comparative study between various recent techniques of big data privacy is also done as well.

There is a significant conceptual gap between legal and mathematical thinking around data privacy. The effect is uncertainty as to which technical offerings meet legal standards. This uncertainty is exacerbated by a litany of successful privacy attacks demonstrating that traditional statistical disclosure limitation techniques often fall short of the privacy envisioned by regulators. We define “predicate singling out,” a type of privacy attack intended to capture the concept of singling out appearing in the General Data Protection Regulation (GDPR). An adversary predicate singles out a dataset x using the output of a data-release mechanism M(x) if it finds a predicate p matching exactly one row in x with probability much better than a statistical baseline. A data-release mechanism that precludes such attacks is “secure against predicate singling out” (PSO secure). We argue that PSO security is a mathematical concept with legal consequences. Any data-release mechanism that purports to “render anonymous” personal data under the GDPR must prevent singling out and, hence, must be PSO secure. We analyze the properties of PSO security, showing that it fails to compose. Namely, a combination of more than logarithmically many exact counts, each individually PSO secure, facilitates predicate singling out. Finally, we ask whether differential privacy and k-anonymity are PSO secure. Leveraging a connection to statistical generalization, we show that differential privacy implies PSO security. However, and in contrast with current legal guidance, k-anonymity does not: There exists a simple predicate singling out attack under mild assumptions on the k-anonymizer and the data distribution.

Darkness can conceal identity and encourage moral transgressions; it may also induce a psychological feeling of illusory anonymity that disinhibits dishonest and self-interested behavior regardless of actual anonymity. Three experiments provided empirical evidence supporting this prediction. In Experiment I, participants in a room with slightly dimmed lighting cheated more and thus earned more undeserved money than those in a well-lit room. In Experiment 2, participants wearing sunglasses behaved more selfishly than those wearing clear glasses. Finally, in Experiment 3, an illusory sense of anonymity mediated the relationship between darkness and self-interested behaviors. Across all three experiments, darkness had no bearing on actual anonymity, yet it still increased morally questionable behaviors. We suggest that the experience of darkness, even when subtle, may induce a sense of anonymity that is not proportionate to actual anonymity in a given situation.

This survey reviews the two most prominent group-oriented anonymous signature schemes and analyzes the existing approaches for their problem: balancing anonymity against traceability. Group signatures and ring signatures are the two leading competitive signature schemes with a rich body of research. Both group and ring signatures enable user anonymity with group settings. Any group user can produce a signature while hiding his identity in a group. Although group signatures have predefined group settings, ring signatures allow users to form ad-hoc groups. Preserving user identities provided an advantage for group and ring signatures. Thus, presently many applications utilize them. However, standard group signatures enable an authority to freely revoke signers’ anonymity. Thus, the authority might weaken the anonymity of innocent users. On the other hand, traditional ring signatures maintain permanent user anonymity, allowing space for malicious user activities; thus achieving the requirements of privacy-preserved traceability in group signatures and controlled anonymity in ring signatures has become desirable. This paper reviews group and ring signatures and explores the existing approaches that address the identification of malicious user activities. We selected many papers that discuss balancing user tracing and anonymity in group and ring signatures. Since this paper scrutinizes both signatures from their basic idea to obstacles including tracing users, it provides readers a broad synthesis of information about two signature schemes with the knowledge of current approaches to balance excessive traceability in group signatures and extreme anonymity in ring signatures. This paper will also shape the future research directions of two critical signature schemes that require more awareness.

Bitcoin is a cryptocurrency whose transactions are recorded on a distributed, openly accessible ledger. On the Bitcoin Blockchain, an owning entity's real-world identity is hidden behind a pseudonym, a so-called address. Therefore, Bitcoin is widely assumed to provide a high degree of anonymity, which is a driver for its frequent use for illicit activities. This paper presents a novel approach for de-anonymizing the Bitcoin Blockchain by using Supervised Machine Learning to predict the type of yet-unidentified entities. We utilized a sample of 957 entities (with ≈385 million transactions), whose identity and type had been revealed, as training set data and built classifiers differentiating among 12 categories. Our main finding is that we can indeed predict the type of a yet-unidentified entity. Using the Gradient Boosting algorithm with default parameters, we achieve a mean cross-validation accuracy of 80.42% and F1-score of ≈79.64%. We show two examples, one where we predict on a set of 22 clusters that are suspected to be related to cybercriminal activities, and another where we classify 153,293 clusters to provide an estimation of the activity on the Bitcoin ecosystem. We discuss the potential applications of our method for organizational regulation and compliance, societal implications, outline study limitations, and propose future research directions. A prototype implementation of our method for organizational use is included in the appendix.

Information systems researchers have shown an increasing interest in the notion of sociomateriality. In this paper, we continue this exploration by focusing specifically on entanglement: the inseparability of meaning and matter. Our particular approach is differentiated by its grounding in a relational and performative ontology, and its use of agential realism. We explore some of the key ideas of entanglement through a comparison of two phenomena in the travel sector: an institutionalized accreditation scheme offered by the AA and an online social media website hosted by TripAdvisor. Our analysis centers on the production of anonymity in these two practices of hotel evaluation. By examining how anonymity is constituted through an entanglement of matter and meaning, we challenge the predominantly social treatments of anonymity to date and draw attention to the uncertainties and outcomes generated by specific performances of anonymity in practice. In closing, we consider what the particular agential realist concept of entanglement entails for understanding anonymity, and discuss its implications for research practice.

During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.