Definition Of Privacy Research Articles

Re-reading Westin

Abstract Alan Westin’s work Privacy and Freedom remains foundational to the field of privacy, and Westin is frequently cited for his definition of privacy as control over personal information. However, Westin’s full definition of privacy is much more complex than this statement, describing four states of privacy (solitude, intimacy, anonymity, and reserve) that one achieves through physical or psychological means. The “claim” of privacy involves negotiating a balance between a desire for disclosure and social participation and a desire for withdrawal into one of the “states” of privacy. Influencing this adjustment process are social norms (and surveillance to enforce social norms), environmental conditions, and the curiosity of others. In this Article, I draw upon this complexity in order to reread Westin’s definition of privacy as a claim of control over personal information and use this rereading to understand how the law should protect and promote privacy in the twenty-first century. I argue that the law should focus on securing meaningful privacy choices rather than on individual control over personal information. Meaningful choice requires that our informational infrastructure, and the social practices that it enables, make states of privacy available for choice along with the means of attaining them. To enable such meaningful individual choice, we need to shift our attention from a focus on individuals to a more systemic focus on our public norms and built infrastructure. Otherwise we risk protecting a narrow understanding of individual control, while ignoring a more general and systematic erosion of privacy.

Synthesis and Satisfaction: How Philosophy Scholarship Matters

Abstract Privacy and technology clash in the courts. I elaborate the example of Puttaswamy v Union of India (2017), an example from the High Court of India, whose sweeping and inclusive jurisprudential style raises starkly the question of the influence that academic philosophers and other scholars have over how legitimate societal interests in exploiting information technology and protecting personal privacy are “balanced” by the courts. Philosophers will be satisfied to see that their theories are acknowledged in a landmark national decision finding that India’s 1.3 billion people have a constitutional, fundamental right to privacy that constrains a challenged government biometric identification system. Some scholars will appreciate the inclusive definition of privacy, which included decisional privacy, combined with the treatment of privacy as a paramount human good meriting the protection of fundamental rights. But some academic philosophers are potentially disappointed that the Court synthesizes rather than differentiates among their competing theories, concepts, and definitions, and, in the end, relies upon liberal Enlightenment ideals that some scholars have argued are singularly ill-suited for the twenty-first century.

Biometric Technology and Ethics: Beyond Security Applications

Biometric technology was once the purview of security, with face recognition and fingerprint scans used for identification and law enforcement. This is no longer the case; biometrics is increasingly used for commercial and civil applications. Due to the widespread diffusion of biometrics, it is important to address the ethical issues inherent to the development and deployment of the technology. This article explores the burgeoning research on biometrics for non-security purposes and the ethical implications for organizations. This will be achieved by reviewing the literature on biometrics and business ethics and drawing from disciplines such as computer ethics to inform a more robust discussion of key themes. Although there are many ethical concerns, privacy is the key issue, with associated themes. These include definitions of privacy, the privacy paradox, informed consent, regulatory frameworks and guidelines, and discrimination. Despite the proliferation of biometric technology, there is little empirical research on applied biometrics and business ethics. As such, there are several avenues for research to improve understanding of the ethical implications of using this technology.

Privacy-Preserving Average Consensus: Privacy Analysis and Algorithm Design

Privacy-preserving average consensus aims to guarantee the privacy of initial states and asymptotic consensus on the exact average of the initial values. In this paper, it is achieved by adding variance-decaying and zero-sum random noises to the consensus process. However, there is lack of theoretical analysis to quantify the degree of the data privacy protection. In this paper, we introduce the maximum disclosure probability that other nodes can infer one node's initial state within a given small interval to quantify the data privacy. We utilize a novel privacy definition, named $(\alpha, \beta)$ -data-privacy, to depict the relationship between the maximum disclosure probability and the estimation accuracy. Then, we prove that the general privacy-preserving average consensus provides $(\alpha, \beta)$ -data-privacy, and obtain the closed-form expression of the relationship between $\alpha$ and $\beta$ given the noise distribution. We reveal that the added noise with a uniform distribution is optimal in terms of achieving the highest $(\alpha, \beta)$ -data-privacy. We also prove that under what condition, the data-privacy will be compromised. Finally, an optimal privacy-preserving average consensus algorithm is proposed to achieve the highest $(\alpha, \beta)$ -data-privacy. Simulations verify the analytical results.

Right to privacy: a review of international and national legislation

The article explores the role and importance of confidential information in the life of modern society, focuses on the problems of ensuring the legal protection of confidential information. The degree of personal freedom in the state, democracy and humanity of the political regime depends on how guaranteed the confidentiality of information is, the secrets of the private life of citizens, how deeply a state can penetrate the content of these secrets. As a result of the study, theoretical statements were formulated proving the existence of the institution of confidential information, its significance in the development of communicative relations was determined, a classification of existing types of confidential information in the legal system of the Republic of Kazakhstan was proposed with substantiation of its practical significance, the definition of secrecy in legislation was outlined, characteristics. Many of these problems have not been previously studied independently or are not sufficiently developed or require rethinking in relation to the new conditions of life in the country.
 Recently, there have been positive developments in terms of overcoming the previously established stereotypes of the secrecy cult. Many information barriers have been eliminated; methods of administrative-command management of information flows are eliminated. The general civilization process of creating the global information space implies greater openness of states. At the same time, the formation of a new statehood based on the principles of democracy, legality, the desire for more active cooperation with foreign countries based on the openness of the parties does not exclude the need to maintain state secrets and other types of confidential information.

Wybrane aspekty tajemnicy w postępowaniu z udziałem nieletniego

This article contains an analysis of selected aspects of secrecy in proceedings with the participation of minors. The analysis of selected legal institutions in the context of secrecy – which is the essence of this article – is preceded by an attempt at explaining the notion of secrecy, including professional secrecy, in the context of proceedings with the participation of minors. It would seem that identifying the elements that make up the essence of secrecy should not cause much trouble: everyone has at some point revealed his/her secret to another person or has been entrusted with one. Nevertheless it is not easy to define this concept in a precise and non-arbitrary way. The situation becomes even more complicated when the secret has anything to do with the law, because even though the legislator defines professional secrecy in several statutes, these definitions do not mention the obligation of confidentiality as such, being limited to determining the scope of such obligation. After the analysis of the definition of secrecy, including professional secrecy, beginning with the dictionary definition and ending with the author’s attempt at formulating her own definition of professional secrecy, the remaining part of the article is devoted to selected aspects of secrecy in criminal proceedings with the participation of a minor, that is, closing the hearing with the participation of a minor to the public and opinions of experts about minors in the context of professional secrecy.

Privacy Preserving Collaborative Computing: Heterogeneous Privacy Guarantee and Efficient Incentive Mechanism

Collaborative computing uses multiple data servers to jointly complete data analysis, e.g., statistical analysis and inference. One major obstruction for it lies in privacy concern, which is directly associated with nodes’ participation and the fidelity of received data. Existing privacy-preserving paradigms for cloud computing and distributed data aggregation only provide nodes with homogeneous privacy protection without consideration of nodes’ diverse trust degrees to different data servers. We propose a two-phase framework that computes the average value while preserving heterogeneous privacy for nodes’ private data. The new challenge is that in the premise of meeting privacy requirements, we should guarantee the proposed framework has the same computation accuracy with existing privacy-aware solutions. In this paper, nodes obtain heterogeneous privacy protection in the face of different data servers through one-shot noise perturbation. Based on the definition of KL privacy, we derive the analytical expressions of the privacy preserving degrees (PPDs) and quantify the relation between different PPDs. Then, we obtain the closed-form expression of computation accuracy. Furthermore, an efficient incentive mechanism is proposed to achieve optimized computation accuracy when data servers have fixed budgets. Finally, extensive simulations are conducted to verify the obtained theoretical results.

Smart Home Users Perception on Sustainable Urban Living and Legal Challenges in Malaysia

Smart homes can improve life by providing comfort, leisure, safety, and healthcare to residents. Nevertheless, the smart home concept depends on the integration on human and non-human intelligence, which raises a host of questions concerning its implication on legal liability. The legal issues in relation to definition of smart home, data protection, privacy, liability and insurance coverage. The aims of this paper is to discuss the perception of smart home users on the sustainable urban living and its challenges. This is a qualitative study and involves a small survey among smart home users in Kuala Lumpur. The findings show that there is no legal specific definition of smart home, there is no specific standard or specification issued by any regulatory bodies to regulate the network or appliances being used in smart homes. Majority of residents agreed that there are threats of privacy to smart home users. And privacy is not guaranteed and majority of residents agreed that their smart home and devices is not covered by insurance policy. Hence, a policy and specific law or at least provisions regarding smart home must be drafted or inserted in the present statute initiated by the Ministry of Urban Wellbeing, Housing, and Local Government of Malaysia.

Statistical Disclosure Limitation: New Directions and Challenges

An overview of traditional types of data dissemination at statistical agencies is provided including definitions of disclosure risks, the quantification of disclosure risk and data utility and common statistical disclosure limitation (SDL) methods. However, with technological advancements and the increasing push by governments for openand accessible data, new forms of data dissemination are currently being explored. We focus on web-based applications such as flexible table builders and remote analysis servers, synthetic data and remote access. Many of these applications introduce new challenges for statistical agencies as they are gradually relinquishing some of their control on what data is released. There is now more recognition of the need for perturbative methods to protect the confidentiality of data subjects. These new forms of data dissemination are changing the landscape of how disclosure risks are conceptualized and the types of SDL methods that need to be applied to protect thedata. In particular, inferential disclosure is the main disclosure risk of concern and encompasses the traditional types of disclosure risks based on identity and attribute disclosures. These challenges have led to statisticians exploring the computer science definition of differential privacy and privacy- by-design applications. We explore how differential privacy can be a useful addition to the current SDL framework within statistical agencies.

Is privacy privacy?

This position paper observes how different technical and normative conceptions of privacy have evolved in parallel and describes the practical challenges that these divergent approaches pose. Notably, past technologies relied on intuitive, heuristic understandings of privacy that have since been shown not to satisfy expectations for privacy protection. With computations ubiquitously integrated in almost every aspect of our lives, it is increasingly important to ensure that privacy technologies provide protection that is in line with relevant social norms and normative expectations. Similarly, it is also important to examine social norms and normative expectations with respect to the evolving scientific study of privacy. To this end, we argue for a rigorous analysis of the mapping from normative to technical concepts of privacy and vice versa. We review the landscape of normative and technical definitions of privacy and discuss specific examples of gaps between definitions that are relevant in the context of privacy in statistical computation. We then identify opportunities for overcoming their differences in the design of new approaches to protecting privacy in accordance with both technical and normative standards.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.

How do older adults experience intergenerational relationships? Different cultures, ambivalent feelings

ABSTRACTObjective: Intergenerational relationships are central for older adults’ well-being. In spite of the reiterated value of intergenerational interactions between older adults and grandchildren, there is paucity of research that has analyzed these relationships at a cross-national level. The current study purports to analyze how older adults conceptualize intergenerational relationships.Method: In this qualitative study, in-depth interviews were conducted with 316 older adults, aged 65–102, currently living at home representing three different nationalities. Verbatim transcripts were examined.Results: Data analysis generated six themes representing intergenerational relationships: affection and reward; interest and integration; quality of grandparent–grandchild interaction; definitions of privacy and boundaries; provision of support; and obligations regarding childcare, supportive, and conflictual.Conclusions: The empirical findings indicated older adults’ ambivalence over intergenerational relationships and contradictory expectations of support from grandchildren.

Privacy-preserving mechanisms for k-modes clustering

Clustering categorical data is an important data mining task with rich applications. As an extension of k-means applied to categorical data, the k-modes algorithm became a popular clustering tool due to its simplicity and efficiency. A lot of improvements for k-modes such as better initialization techniques or more effective dissimilarity scores have been proposed recently. However, the problem of running the k-modes in private manners is rarely considered. In this paper, we address the privacy-preserving k-modes problem using differential privacy, a formal and rigorous definition of privacy for data publication. Differential privacy guarantees that the existence of any item in the input dataset is indistinguishable by looking at the computation output. We analyze the challenges of differentially private k-modes with regard to the k-means counterpart. Then we propose several schemes in both interactive and non-interactive settings. We prove that our mechanisms satisfy differential privacy and run linearly in the number of data points. Evaluation over fifteen real datasets shows that we can achieve useful privacy-preserving clustering outputs. In terms of clustering cost, the interactive approaches perform better than the non-interactive schemes and the solution adapted from k-means.

Differentially private model publishing in cyber physical systems

With the development of Cyber Physical Systems, privacy issues become an important topics in the past few years. It is worthwhile to apply differential privacy, one of the most influential privacy definitions, in cyber physical system. However, as the essential idea of differential privacy is to release query results rather than entire datasets, a large volume of noise has to be introduced. To provide high quality services we need to decrease the correlation between large sets of queries, while to predict on newly entered queries. This paper transfers the data publishing problem in cyber physical systems to a machine learning problem, in which a prediction model will be shared with clients. The predict model is used to answer current submitted queries and predict results for newly entered queries from the public.

Cryptanalysis of Morillo–Obrador polynomial delegation schemes

Verifiable computation (VC) allows a client to outsource (delegate) the computation of a function f on an input x to a server and then verify the server's results with substantially less time than computing f(x) from scratch. The security of VC requires no efficient adversary can persuade the client to accept any wrong results. Morillo and Obrador (PST 2013) proposed three VC schemes for outsourcing the computation of polynomial functions and claimed that all schemes are secure under the decisional subgroup membership assumption. The authors show a simple attack against the security of their first scheme and then extend the attack to the other two schemes. Morillo and Obrador (PST 2013) also claimed that their third scheme keeps the client's input private under the square root assumption. The authors show that this is not true under the standard definition of input privacy. In particular, a curious server can extract the client's input x, if the x is not too large. The authors' results show that Morillo-Obrador schemes cannot be used in the polynomial delegation.

Privacy for whom?: a feminist intervention in online research practice

ABSTRACTAs shifts in technology and culture have complicated traditional definitions of privacy, researchers need new approaches to navigating privacy in online contexts. In this article, we argue that the experiences and perceptions of vulnerable groups must form the starting point for online researchers’ ethical decision-making, regardless of whether their research population qualifies as ‘vulnerable.’ This is especially important in spaces where privacy violations put people, particularly marginalized individuals, at risk for online harassment and abuse, among other harms. We seek to intervene in online research practices by putting forth a feminist approach to privacy, drawing on two studies related to online harassment. Specifically, we argue that feminist theory and methodology inform an approach to privacy that (a) starts from the lives of socially and politically vulnerable groups, (b) takes an intersectional approach to analyzing power relations, and (c) draws on a moral imperative of care and responsibility in enacting feminist principles of context, dialogue, and reflexivity throughout the research process. In doing so, we offer questions to prompt critical reflection on privacy concerns in online research.

A differential privacy DNA motif finding method based on closed frequent patterns

As one of the basic research methods of bioinformatics, DNA motif finding is of great significance to the study of mechanisms for regulating gene expression and the discovery of biological functional sites. However, because of the high sensitivity of DNA data, the privacy disclosure of these data during motif finding has become a bottleneck in the field of gene research. Meanwhile, traditional privacy protection data mining methods cannot deal with DNA sequences directly, and the existing private motif finding methods usually decrease the utility of the results. To solve these problems, we propose a high-utility motif finding algorithm based on $$\epsilon $$ -differential privacy, which is known as a rigorous definition of privacy with meaningful privacy guarantees in the presence of arbitrary external information. Our solution makes use of the closed frequent pattern set to reduce redundant motifs of result sets and obtain accurate motifs results, satisfying $$\epsilon $$ -differential privacy. Furthermore, a post-processing method based on the best linear unbiased estimate is used to optimize the utility of noisy consolidated motif support. Experiments on real-life DNA sequence datasets confirm that our algorithm is superior to the existing algorithms in terms of utility.

Location Privacy and Its Applications: A Systematic Study

This paper surveys the current research status of location privacy issues in mobile applications. The survey spans five aspects of study: the definition of location privacy, attacks and adversaries, mechanisms to preserve the privacy of locations, location privacy metrics, and the current status of location-based applications. Through this comprehensive review, all the interrelated aspects of location privacy are integrated into a unified framework. Additionally, the current research progress in each area is reviewed individually, and the links between existing academic research and its practical applications are identified. This in-depth analysis of the current state-of-play in location privacy is designed to provide a solid foundation for future studies in the field.

Privacy, consent and vehicular ad hoc networks (VANETs)

The consent model of privacy protection assumes that individuals control their personal information and are able to assess the risks associated with data sharing. The model is attractive for policy-makers and automakers because it has the effect of glossing over the conceptual ambiguities that are latent in definitions of privacy. Instead of formulating a substantive and normative position on what constitutes a reasonable expectation of privacy in the circumstance, individuals are said to have control over their data. Organizations have obligations to respect rights to notice, access and consent regarding the collection, use and disclosure of personal data once that data has been shared. The policy goal becomes how to provide individuals with control over their personal data in the consent model of privacy protection. This paper argues that the privacy issues raised by vehicular ad hoc networks make this approach increasingly untenable. It is argued that substantive rules that establish a basic set of privacy norms regarding the collection, use and disclosure of data are necessary. This can be realized in part via a privacy code of practice for the connected vehicle. This paper first explores the relationship between privacy, consent and personal information in relation to the connected car. This is followed by a description of vehicular ad hoc networks and a survey of the technical proposals aimed at securing data. The privacy issues that will likely remain unsolved by enhancing individual consent are then discussed. The last section provides some direction on how a code of practice can assist in determining when individual consent will need to be enhanced and when alternatives to consent will need to be implemented.

Calibrating Noise to Sensitivity in Private Data Analysis

We continue a line of research initiated in Dinur and Nissim (2003); Dwork and Nissim (2004); and Blum et al. (2005) on privacy-preserving statistical databases.
 Consider a trusted server that holds a database of sensitive information. Given a query function $f$ mapping databases to reals, the so-called {\em true answer} is the result of applying $f$ to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user.
 Previous work focused on the case of noisy sums, in which $f = \sum_i g(x_i)$, where $x_i$ denotes the $i$th row of the database and $g$ maps database rows to $[0,1]$. We extend the study to general functions $f$, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the {\em sensitivity} of the function $f$. Roughly speaking, this is the amount that any single argument to $f$ can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case.
 The first step is a very clean definition of privacy---now known as differential privacy---and measure of its loss. We also provide a set of tools for designing and combining differentially private algorithms, permitting the construction of complex differentially private analytical tools from simple differentially private primitives.
 Finally, we obtain separation results showing the increased value of interactive statistical release mechanisms over non-interactive ones.

Platform privacies: Governance, collaboration, and the different meanings of “privacy” in iOS and Android development

Mobile application design can have a tremendous impact on consumer privacy. But how do mobile developers learn what constitutes privacy? We analyze discussions about privacy on two major developer forums: one for iOS and one for Android. We find that the different platforms produce markedly different definitions of privacy. For iOS developers, Apple is a gatekeeper, controlling market access. The meaning of “privacy” shifts as developers try to interpret Apple’s policy guidance. For Android developers, Google is one data-collecting adversary among many. Privacy becomes a set of defensive features through which developers respond to a data-driven economy’s unequal distribution of power. By focusing on the development cultures arising from each platform, we highlight the power differentials inherent in “privacy by design” approaches, illustrating the role of platforms not only as intermediaries for privacy-sensitive content but also as regulators who help define what privacy is and how it works.