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Artículos de revistas sobre el tema "Privacy"

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Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 14 de septiembre de 2024

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1

Serwin, Andrew. "Privacy 3.0-The Principle of Proportionality". University of Michigan Journal of Law Reform, n.º 42.4 (2009): 869. http://dx.doi.org/10.36646/mjlr.42.4.privacy.

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Individual concern over privacy has existed as long as humans have said or done things they do not wish others to know about. In their groundbreaking law review article The Right to Privacy, Warren and Brandeis posited that the common law should protect an individual's right to privacy under a right formulated as the right to be let alone-Privacy 1.0. As technology advanced and societal values also changed, a belief surfaced that the Warren and Brandeis formulation did not provide sufficient structure for the development of privacy laws. As such, a second theoretical construct of privacy, Privacy 2.0 as expressed in Dean Prosser's work Privacy was created. Dean Prosser continued (or expanded) upon the concepts formulated by Warren and Brandeis, particularly in emphasizing the role of common law in protecting privacy. These works, while influential in their time, do not account for paradigm shifts in technology, or, perhaps more importantly, changes in how people live their lives. The unending advance of technology and changes in societal norms fundamentally dictate that privacy theory must change over time, or it will lose its relevance. Indeed, in today ' Web 2.0 world where many people instantly share very private aspects of their lives, one can hardly imagine a privacy concept more foreign than the right to be let alone. The question confronting modern-day privacy scholars is this: Can a common law based theory adequately address the shifting societal norms and rapid technological changes of today's Web 2.0 world where legislatures and government agencies, not courts, are more proactive on privacy protections? This Article argues that the answer is no and instead argues that the overarching principle of privacy of today should not be the right to be let alone, but rather the principle of proportionality. This is Privacy 3. 0.

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2

Cohen, Julie E. "Turning Privacy Inside Out". Theoretical Inquiries in Law 20, n.º 1 (16 de marzo de 2019): 1–31. http://dx.doi.org/10.1515/til-2019-0002.

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Abstract The problem of theorizing privacy moves on two levels, the first consisting of an inadequate conceptual vocabulary and the second consisting of an inadequate institutional grammar. Privacy rights are supposed to protect individual subjects, and so conventional ways of understanding privacy are subject-centered, but subject-centered approaches to theorizing privacy also wrestle with deeply embedded contradictions. And privacy’s most enduring institutional failure modes flow from its insistence on placing the individual and individualized control at the center. Strategies for rescuing privacy from irrelevance involve inverting both established ways of talking about privacy rights and established conventions for designing institutions to protect them. In terms of theory, turning privacy inside out entails focusing on the conditions that are needed to produce sufficiently private and privacy-valuing subjects. Institutionally, turning privacy inside out entails focusing on the design, production, and operational practices most likely to instantiate and preserve those conditions.

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3

Lemley, Mark. "Privacy, Property, and Publicity". Michigan Law Review, n.º 117.6 (2019): 1153. http://dx.doi.org/10.36644/mlr.117.6.privacy.

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4

Culshaw, Helen. "Private sector libraries and privacy". ANZTLA EJournal, n.º 49 (29 de abril de 2019): 10–13. http://dx.doi.org/10.31046/anztla.v0i49.1196.

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5

Munro, Moira y Ruth Madigan. "Privacy in the private sphere". Housing Studies 8, n.º 1 (enero de 1993): 29–45. http://dx.doi.org/10.1080/02673039308720748.

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6

Etzioni, Amitai. "Privacy and the private realm". Innovation: The European Journal of Social Science Research 25, n.º 1 (marzo de 2012): 57–66. http://dx.doi.org/10.1080/13511610.2012.655574.

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7

Darr, Kurt. "Privacy—Private Lives, Public Lives". Hospital Topics 81, n.º 3 (enero de 2003): 29–32. http://dx.doi.org/10.1080/00185860309598025.

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8

Bläser, Markus, Andreas Jakoby, Maciej Liśkiewicz y Bodo Manthey. "Privacy in Non-private Environments". Theory of Computing Systems 48, n.º 1 (16 de octubre de 2009): 211–45. http://dx.doi.org/10.1007/s00224-009-9243-1.

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9

Nissim, Kobbi y Alexandra Wood. "Is privacy privacy ?" Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, n.º 2128 (6 de agosto de 2018): 20170358. http://dx.doi.org/10.1098/rsta.2017.0358.

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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’.

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10

Sari, Putri Widya. "Pengungkapan Diri dalam Perseteruan Youtuber Kembar Tasya Farasya dan Tasyi Athasyia". Jurnal Komunikasi Nusantara 5, n.º 1 (1 de junio de 2023): 67–77. http://dx.doi.org/10.33366/jkn.v5i1.244.

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Communication Privacy Management (CPM) theory is a theory that addresses the tension between openness and privacy, between "public" and "private" in a relationship. CPM theory in previous studies has been widely used in social media. This study aims to examine the application of privacy rules and restrictions on the disclosure of private information to the public based on the CPM theory carried out by celebrity and YouTuber Tasyi Athasyia through her social media account in a dispute with her twin Tasya Farasya. The method used in this study is a qualitative content analysis method by analyzing several clarifying videos from the YouTube account Tasyi Athasyia. The object of research on this topic is the disclosure of private information disclosed by Tasyi on her Instagram social media account and retold or narrated on her clarification YouTube video. This study found that in disclosing private information, Tasyi Athasyia has implemented privacy rules and restrictions that serve as guidelines for disclosing to the public with the aim of disclosing private information in the form of understanding and support from the public who previously insulted her. The CPM theory is useful for operationalizing the nature of privacy and has the assumption that communication takes place with cost and rewards considerations emphasizing that the privacy aspect is something that is used in communicating to achieve the ultimate goal rewards. Abstrak Teori Communication Privacy Management (CPM) adalah teori yang membahas ketegangan antara keterbukaan dan privasi, antara "publik" dan "pribadi" dalam suatu hubungan. Teori CPM pada penelitian sebelumnya banyak digunakan dalam media sosial. Penelitian ini bertujuan untuk mengkaji penerapan aturan dan batasan privasi dari pengungkapan informasi privat kepada publik berdasarkan pada teori CPM yang dilakukan oleh selebgram dan youtuber Tasyi Athasyia melalui akun sosial media yang dimilikinya dalam perseteruan dengan kembarannya Tasya Farasya. Metode yang digunakan dalam penelitian ini adalah metode analisis isi yang bersifat kualitatif dengan menganalisis beberapa video klarifikasi dari akun Youtube Tasyi Athasyia. Objek penelitian pada topik ini yaitu pengungkapan informasi privat yang diungkapkan Tasyi pada akun media sosial Instagram miliknya dan diceritakan atau dinarasikan kembali pada video youtube klarifikasinya. Penelitian ini menemukan hasil bahwa dalam pengungkapan informasi privat yang dilakukan Tasyi Athasyia telah menerapkan aturan dan batasan privasi yang menjadi pedoman dalam pengungkapan kepada publik dengan tujuan dari pengungkapan informasi privat tersebut berupa pengertian dan dukungan dari publik yang sebelumnya menghujatnya. Teori CPM berguna untuk mengoperasionalkan sifat privasi dan memiliki asumsi bahwa komunikasi berlangsung dengan adanya pertimbangan cost and rewards yang menekankan bahwa aspek privasi merupakan hal yang digunakan dalam berkomunikasi untuk mencapai rewards tujuan akhirnya.

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11

Nahmias, Yifat. "Privacy Preserving Social Norm Nudges". Michigan Technology Law Review, n.º 26.1 (2019): 43. http://dx.doi.org/10.36645/mtlr.26.1.privacy.

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Nudges comprise a key component of the regulatory toolbox. Both the public and private sectors use nudges extensively in various domains, ranging from environmental regulation to health, food and financial regulation. This article focuses on a particular type of nudge: social norm nudges. It discusses, for the first time, the privacy risks of such nudges. Social norm nudges induce behavioral change by capitalizing on people’s desire to fit in with others, on their predisposition to social conformity, and on their susceptibility to the way information is framed. In order to design effective social norm nudges, personal information about individuals and their behavior must be collected, processed, and later disseminated (usually in some aggregated form). Thus, the use of social norm nudges opens up the possibility for privacy threats. Despite the significant privacy concerns raised by social norm nudges, research on the topic has been scarce. This article makes two contributions to the understanding of the privacy risks underlying the use of social norm nudges. The first contribution is analytic: it demonstrates that using social norm nudges can pose a threat to individuals’ privacy through re-identification of anonymized data. This risk was demonstrated in other contexts (e.g. Netflix recommendation contest). The second contribution is policy oriented: it argues that the strategy of differential privacy can be used to mitigate these privacy risks and offer a way to employ social norms nudges while protecting individuals’ privacy.

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12

Thrasher, Karin. "The Privacy Cost of Currency". Michigan Journal of International Law, n.º 42.2 (2021): 403. http://dx.doi.org/10.36642/mjil.42.2.privacy.

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Banknotes, or cash, can be used continuously by any person for nearly every transaction and provide anonymity for the parties. However, as digitization increases, the role and form of money is changing. In response to pressure produced by the increase in new forms of money and the potential for a cashless society, states are exploring potential substitutes to cash. Governments have begun to investigate the intersection of digitization and fiat currency: Central Bank Digital Currencies (“CBDC”). States have begun researching and developing CBDCs to serve in lieu of cash. Central banks are analyzing the potential for a CBDC that could be made available to the public and serve as a substitute for cash by providing an alternate, safe, and robust payment instrument. However, the greatest attribute of cash is that it protects purchaser anonymity. Fully eliminating cash, without a substitute that safeguards anonymity, would undermine privacy of individuals. The creation of a CBDC in response to the potential cashless society raises the question whether the anonymity previously provided by cash must be safeguarded by the state. This note posits that a central bank in a cashless society must opt for the token-based form of CBDC, which provides the most privacy to individuals. States that choose an account-based CBDC will be in violation of fundamental international privacy principles. This note begins by drawing the crucial distinction between account-based and token-based currencies. Then, this note argues that the broad right to privacy in the digital age is inclusive of personal financial data; this data is subject to the lawful and arbitrary standards of article 17 of the International Covenant on Civil and Political Rights (“ICCPR”). Applying the ICCPR framework, it becomes abundantly clear that the privacy of individuals must be protected, even in the rapidly changing landscape of payments in the digital age.

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13

Dawes, Simon. "Privacy and the public/private dichotomy". Thesis Eleven 107, n.º 1 (noviembre de 2011): 115–24. http://dx.doi.org/10.1177/0725513611424812.

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This essay reviews Helen Nissenbaum’s Privacy in Context (2010), focusing in particular on her dismissal of the public/private dichotomy. Taking issue with the problem she constructs of ‘privacy in public’, her unitary reading of the dichotomy and ‘socializing’ of the value of privacy, or what she calls ‘contextual integrity’, and her treatment of technology in the abstract, the essay then goes on to argue that the framework she proposes is incapable of addressing the contemporary incursion of market logic into every other aspect of social and political life in the digital economy, and therefore of protecting privacy at all. The essay concludes with an insistence on the need to approach contextual privacy problems from a political economic perspective and with a political conception of privacy, and for that to be founded upon a protean appreciation of the public/private dichotomy.

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14

Peng, Shin-yi. "Public–Private Interactions in Privacy Governance". Laws 11, n.º 6 (26 de octubre de 2022): 80. http://dx.doi.org/10.3390/laws11060080.

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This paper addresses the possible roles of private actors when privacy paradigms are in flux. If the traditional “informed consent”-based government-dominated approaches are ill-suited to the big data ecosystem, can private governance fill the gap created by state regulation? In reality, how is public–private partnership being implemented in the privacy protection frameworks? This paper uses cases from APEC’s Cross-Border Privacy Rules (CBPR) and the EU’s General Data Protection Regulation (GDPR) as models for exploration. The analysis in this paper demonstrates the fluidity of interactions across public and private governance realms. Self-regulation and state regulation are opposing ends of a regulatory continuum, with CBPR-type “collaboration” and GDPR-type “coordination” falling somewhere in the middle. The author concludes that there is an evident gap between private actors’ potential governing functions and their current roles in privacy protection regimes. Looking to the future, technological developments and market changes call for further public–private convergence in privacy governance, allowing the public authority and the private sector to simultaneously reshape global privacy norms.

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15

Sanfilippo, Madelyn R., Yan Shvartzshnaider, Irwin Reyes, Helen Nissenbaum y Serge Egelman. "Disaster privacy/privacy disaster". Journal of the Association for Information Science and Technology 71, n.º 9 (13 de marzo de 2020): 1002–14. http://dx.doi.org/10.1002/asi.24353.

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16

Guzmán-Castillo, Adán F., Gabriela Suntaxi, Bryan N. Flores-Sarango y Denys A. Flores. "Towards Designing a Privacy-Oriented Architecture for Managing Personal Identifiable Information". Journal of Internet Services and Information Security 14, n.º 1 (2 de marzo de 2024): 64–84. http://dx.doi.org/10.58346/jisis.2024.i1.005.

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Recent threat reports have warned researchers and security professionals about a shortage of cybersecurity skills to face devastating personal data breaches. As a response, governments have taken on the challenge of proposing specific legislation to protect citizens' privacy while holding information-processing companies accountable for any misuse. However, unauthorized access to such information, or possible negligent destruction of personal records are some issues that cannot be dealt with privacy laws alone. In this research, we introduce the functional requirements to deploy PriVARq, a novel privacy-oriented architecture to proactively manage any consensual submission of personal identifiable information (PII); i.e. during its collection, processing, verification and transference. PriVARq’s main contribution is the balance between legal frameworks and industry-leading security standards to mitigate the former’s shortage of practical solutions to tackle some privacy and security issues when dealing with PII. Consequently, for defining PriVARq’s functional requirements, a privacy-by-design approach is employed which not only considers legislation proposed in Europe and Latin America but also analyzes technical aspects outlined in international security standards. We aim to provide a proactive approach to reduce the shortage of skills and solutions to tackle privacy leakages in public repositories and devise future research venues to implement PriVARq in public and private organizations.

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17

Annabelle Lever. "Privacy, Private Property, and Collective Property". Good Society 21, n.º 1 (2012): 47. http://dx.doi.org/10.5325/goodsociety.21.1.0047.

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18

Chassie, K. "A private matter [privacy in society]". IEEE Potentials 20, n.º 4 (2001): 26. http://dx.doi.org/10.1109/45.969591.

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19

Hasselkus, Amy y Kate Romanow. "Privacy Act Basics for Private Practitioners". ASHA Leader 14, n.º 11 (septiembre de 2009): 3–19. http://dx.doi.org/10.1044/leader.bml.14112009.3.

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20

Strayer, W. Timothy. "Privacy issues in virtual private networks". Computer Communications 27, n.º 6 (abril de 2004): 517–21. http://dx.doi.org/10.1016/j.comcom.2003.08.016.

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21

Lever, Annabelle. "Privacy, Private Property, and Collective Property". Good Society 21, n.º 1 (2012): 47–60. http://dx.doi.org/10.1353/gso.2012.0009.

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22

Ashford, Chris y Mark O'Brien. "Privacy and the public/private divide". Information & Communications Technology Law 17, n.º 1 (marzo de 2008): 1–2. http://dx.doi.org/10.1080/13600830801886950.

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23

Decew, Judith W. "Defending the ?private? in constitutional privacy". Journal of Value Inquiry 21, n.º 3 (agosto de 1987): 171–84. http://dx.doi.org/10.1007/bf00140359.

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24

Fernández Barbudo, Carlos. "Privacidad (digital) = (Digital) Privacy". EUNOMÍA. Revista en Cultura de la Legalidad, n.º 17 (27 de septiembre de 2019): 276. http://dx.doi.org/10.20318/eunomia.2019.5033.

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Resumen: El desarrollo de las tecnologías de la información, y en particular Internet, ha supuesto la aparición de nuevas preocupaciones sociales que plantean la imposibilidad de preservar la privacidad ―que no la intimidad― de la población en el ámbito digital. Esta contribución aborda, en perspectiva histórica, la formación de un nuevo concepto sociopolítico de privacidad que ha sustituido al de intimidad en el ámbito digital. A tal fin se presentan los principales elementos que diferencian a ambos y cuáles son las transformaciones sociotécnicas fundamentales que han posibilitado este cambio conceptual. El desarrollo del texto llevará a defender la idoneidad de una mirada política sobre la privacidad y finaliza con la presentación de algunas propuestas recientes que abogan por entender la privacidad como un problema colectivo.Palabras clave: Espacio público, derecho a la privacidad, intimidad, público/privado, ciberespacio.Abstract: The development of information technologies, and in particular the Internet, has led to the emergence of new social concerns that raise the impossibility of preserving privacy in the digital sphere. This contribution addresses, in historical perspective, the formation of a new socio-political concept of privacy that has replaced the previous one. To this end, the main elements that differentiate both are presented and what are the fundamental sociotechnical transformations that have enabled this conceptual change. The development of the text will lead to defend the suitability of a political view on privacy and ends with the presentation of some recent proposals that advocate understanding privacy as a collective problem.Keywords: Public space, right to privacy, private life, public/private, cyberspace.

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25

Wang, Le, Zao Sun, Xiaoyong Dai, Yixin Zhang y Hai-hua Hu. "Retaining users after privacy invasions". Information Technology & People 32, n.º 6 (2 de diciembre de 2019): 1679–703. http://dx.doi.org/10.1108/itp-01-2018-0020.

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Purpose The purpose of this paper is to facilitate understanding of how to mitigate the privacy concerns of users who have experienced privacy invasions. Design/methodology/approach Drawing on the communication privacy management theory, the authors developed a model suggesting that privacy concerns form through a cognitive process involving threat-coping appraisals, institutional privacy assurances and privacy experiences. The model was tested using data from an empirical survey with 913 randomly selected social media users. Findings Privacy concerns are jointly determined by perceived privacy risks and privacy self-efficacy. The perceived effectiveness of institutional privacy assurances in terms of established privacy policies and privacy protection technology influences the perceptions of privacy risks and privacy self-efficacy. More specifically, privacy invasion experiences are negatively associated with the perceived effectiveness of institutional privacy assurances. Research limitations/implications Privacy concerns are conceptualized as general concerns that reflect an individual’s worry about the possible loss of private information. The specific types of private information were not differentiated. Originality/value This paper is among the first to clarify the specific mechanisms through which privacy invasion experiences influence privacy concerns. Privacy concerns have long been viewed as resulting from individual actions. The study contributes to literature by linking privacy concerns with institutional privacy practice.

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26

Teutsch, Doris, Philipp K. Masur y Sabine Trepte. "Privacy in Mediated and Nonmediated Interpersonal Communication: How Subjective Concepts and Situational Perceptions Influence Behaviors". Social Media + Society 4, n.º 2 (abril de 2018): 205630511876713. http://dx.doi.org/10.1177/2056305118767134.

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New communication media such as social networking sites (SNSs) and instant messengers (IMs) challenge users’ privacy perceptions. Technical infrastructures and the flow of digital information lead to novel privacy risks that individuals are often not acquainted with. Users’ subjective perceptions of privacy may thus be flawed and lead to irrational behavior. In this work, we investigated a concept that has been addressed only implicitly in academic research on privacy: the user’s subjective perception of a given level of privacy. We examined the literature on how privacy perceptions have been conceptualized in traditional theories of privacy and how these conceptualizations are challenged in social media communication. We first qualitatively explored laypeople’s privacy concepts and investigated their subjective perceptions of privacy levels and subsequent private disclosures in different mediated and nonmediated communication settings. Interviews with N = 33 Germans revealed that, similar to academic privacy theories, they tend to conceptualize privacy as control over social, physical, and psychological boundaries. However, trust and other-dependent privacy emerged as important novel aspects for understanding privacy regulation in online communication. We further found that individuals consistently perceived a high level of privacy in face-to-face situations and a low level of privacy in public communication on SNSs. With regard to IMs, however, their answers were mixed: Uncertainty regarding digital communication properties and audiences as well as limited control over the communication setting prevented a reliable and shared perception of the privacy level. With regard to privacy behavior and private disclosures, we found that people tend to adapt their sharing of private information to the perceived level of privacy.

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27

Zhenjiang Zhang, Zhenjiang Zhang y Xiaohua Liu Zhenjiang Zhang. "Cloud-side Collaborative Privacy Protection Based on Differential Privacy". 電腦學刊 32, n.º 4 (agosto de 2021): 239–51. http://dx.doi.org/10.53106/199115992021083204019.

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28

Kovanič, Martin y Samuel Spáč. "Conceptions of Privacy in the Digital Era: Perceptions of Slovak Citizens". Surveillance & Society 20, n.º 2 (15 de junio de 2022): 186–201. http://dx.doi.org/10.24908/ss.v20i2.14099.

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In the digital era, citizens’ daily lives are taking place both in the physical and digital realms. At the same time, even the most mundane activities are increasingly affected by offline as well as online surveillance. The privacy paradigm suggests that there is a difference between the private and public spheres of life, and that with technological advancement the demarcation lines between these spheres have become blurred. As a consequence, citizens’ conceptions of privacy are becoming more fluid, nuanced, context-dependent, and socially determined. This gives rise to a need to reconceptualize what privacy means and how citizens think about its boundaries. To investigate citizens’ conceptions of privacy, we conducted six focus groups in Slovakia aimed at exploring people’s attitudes toward privacy and encompassing their experiences and rationalizations (including possible alterations) of behavior in a variety of everyday environments. The analysis suggests that privacy is a complex phenomenon that is understood as an interplay between different privacy norms guiding specific contexts and more general approaches to privacy. We identify four privacy environments (a controlled private space, a [voluntarily] shared private space, a transactional public space, and a non-controllable public space) and three privacy approaches (the reservations approach, the trade-off approach, and the death of privacy approach) whose interplay constitutes individuals’ conceptions of privacy. In addition, the acceptance of loss of privacy seems to depend on a perception of legitimacy, control over the mechanisms of surveillance that individuals encounter, and trust toward the data processor.

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29

Austin, Lisa M. "Privacy and Private Law: The Dilemma of Justification". McGill Law Journal 55, n.º 2 (16 de diciembre de 2010): 165–210. http://dx.doi.org/10.7202/045084ar.

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In recent years there has been a remarkable convergence across several common law jurisdictions regarding the need to recognize some form of a tort of invasion of privacy, particularly with respect to the publication of private facts. Despite this convergence, the author argues that there remains a palpable “containment anxiety” at play in the jurisprudence that is responsible for a number of recurring tensions regarding the scope of protection. Instead of focusing on the question of how to define privacy, this paper frames the containment anxiety at issue in the cases in terms of a justificatory dilemma rather than a definitional one. Using the work of Mill and Kant, the author argues that if we understand privacy rights as protecting either the value of autonomy or freedom from harm then we can justify a narrow legal right to privacy. Although this can explain the containment anxiety in the jurisprudence, it severely undermines the growing recognition of the importance of privacy. Therefore this paper proposes an alternative justification for privacy rights that is rooted in the value of protecting identity interests, where identity is understood in terms of one's capacity for self-presentation.

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30

Squires, Judith. "Private Lives, Secluded Places: Privacy as Political Possibility". Environment and Planning D: Society and Space 12, n.º 4 (agosto de 1994): 387–401. http://dx.doi.org/10.1068/d120387.

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The theoretical writings that underpin contemporary liberal democracies have all, in varying form, stressed the value of privacy as fundamental to the realisation of a civilised society, Yet it is ever more evident that privacy is now so threatened as to be practically lost to us already. Unless we turn our attention to the task of rethinking the nature of our concern for privacy, and to the possibilities of its realisation and preservation, we may indeed find ourselves bereft of one of our most fundamental values. I make this claim in recognition of the fact that the condition of postmodernity is characterised by forces that would erode many of the spaces and places in which privacy was previously grounded.

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31

COSTEA, Ioan. "Data Privacy Assurance in Virtual Private Networks". International Journal of Information Security and Cybercrime 1, n.º 2 (21 de diciembre de 2012): 40–47. http://dx.doi.org/10.19107/ijisc.2012.02.05.

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32

Collins, Hugh. "The Decline of Privacy in Private Law". Journal of Law and Society 14, n.º 1 (1987): 91. http://dx.doi.org/10.2307/1410299.

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33

Yan, Ziqi, Gang Li y Jiqiang Liu. "Private rank aggregation under local differential privacy". International Journal of Intelligent Systems 35, n.º 10 (22 de julio de 2020): 1492–519. http://dx.doi.org/10.1002/int.22261.

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34

Lai, Jianchang, Yi Mu, Fuchun Guo, Peng Jiang y Willy Susilo. "Privacy-enhanced attribute-based private information retrieval". Information Sciences 454-455 (julio de 2018): 275–91. http://dx.doi.org/10.1016/j.ins.2018.04.084.

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35

Tiwari, Kapil, Nirmalya Sarkar y Jossy P. George. "Confidential Training and Inference using Secure Multi-Party Computation on Vertically Partitioned Dataset". Scalable Computing: Practice and Experience 24, n.º 4 (17 de noviembre de 2023): 1065–76. http://dx.doi.org/10.12694/scpe.v24i4.2220.

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Digitalization across all spheres of life has given rise to issues like data ownership and privacy. Privacy-Preserving Machine Learning (PPML), an active area of research, aims to preserve privacy for machine learning (ML) stakeholders like data owners, ML model owners, and inference users. The Paper, CoTraIn-VPD, proposes private ML inference and training of models for vertically partitioned datasets with Secure Multi-Party Computation (SPMC) and Differential Privacy (DP) techniques. The proposed approach addresses complications linked with the privacy of various ML stakeholders dealing with vertically portioned datasets. This technique is implemented in Python using open-source libraries such as SyMPC (SMPC functions), PyDP (DP aggregations), and CrypTen (secure and private training). The paper uses information privacy measures, including mutual information and KL-Divergence, across different privacy budgets to empirically demonstrate privacy preservation with high ML accuracy and minimal performance cost.

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36

Galindo Q, July. "Privacy by design. Implementing privacy as a good business decision". Revista de Derecho, Comunicaciones y Nuevas Tecnologías 12 (18 de diciembre de 2014): 1–24. http://dx.doi.org/10.15425/redecom.12.2014.11.

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37

Mulligan, Deirdre K., Colin Koopman y Nick Doty. "Privacy is an essentially contested concept: a multi-dimensional analytic for mapping privacy". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, n.º 2083 (28 de diciembre de 2016): 20160118. http://dx.doi.org/10.1098/rsta.2016.0118.

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The meaning of privacy has been much disputed throughout its history in response to wave after wave of new technological capabilities and social configurations. The current round of disputes over privacy fuelled by data science has been a cause of despair for many commentators and a death knell for privacy itself for others. We argue that privacy’s disputes are neither an accidental feature of the concept nor a lamentable condition of its applicability. Privacy is essentially contested. Because it is, privacy is transformable according to changing technological and social conditions. To make productive use of privacy’s essential contestability, we argue for a new approach to privacy research and practical design, focused on the development of conceptual analytics that facilitate dissecting privacy’s multiple uses across multiple contexts. This article is part of the themed issue ‘The ethical impact of data science’.

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38

Boenisch, Franziska, Christopher Mühl, Roy Rinberg, Jannis Ihrig y Adam Dziedzic. "Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees". Proceedings on Privacy Enhancing Technologies 2023, n.º 1 (enero de 2023): 158–76. http://dx.doi.org/10.56553/popets-2023-0010.

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Applying machine learning (ML) to sensitive domains requires privacy protection of the underlying training data through formal privacy frameworks, such as differential privacy (DP). Yet, usually, the privacy of the training data comes at the cost of the resulting ML models' utility. One reason for this is that DP uses one uniform privacy budget epsilon for all training data points, which has to align with the strictest privacy requirement encountered among all data holders. In practice, different data holders have different privacy requirements and data points of data holders with lower requirements can contribute more information to the training process of the ML models. To account for this need, we propose two novel methods based on the Private Aggregation of Teacher Ensembles (PATE) framework to support the training of ML models with individualized privacy guarantees. We formally describe the methods, provide a theoretical analysis of their privacy bounds, and experimentally evaluate their effect on the final model's utility using the MNIST, SVHN, and Adult income datasets. Our empirical results show that the individualized privacy methods yield ML models of higher accuracy than the non-individualized baseline. Thereby, we improve the privacy-utility trade-off in scenarios in which different data holders consent to contribute their sensitive data at different individual privacy levels.

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39

Pan, Ke y Kaiyuan Feng. "Differential Privacy-Enabled Multi-Party Learning with Dynamic Privacy Budget Allocating Strategy". Electronics 12, n.º 3 (28 de enero de 2023): 658. http://dx.doi.org/10.3390/electronics12030658.

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As one of the promising paradigms of decentralized machine learning, multi-party learning has attracted increasing attention, owing to its capability of preventing the privacy of participants from being directly exposed to adversaries. Multi-party learning enables participants to train their model locally without uploading private data to a server. However, recent studies have shown that adversaries may launch a series of attacks on learning models and extract private information about participants by analyzing the shared parameters. Moreover, existing privacy-preserving multi-party learning approaches consume higher total privacy budgets, which poses a considerable challenge to the compromise between privacy guarantees and model utility. To address this issue, this paper explores an adaptive differentially private multi-party learning framework, which incorporates zero-concentrated differential privacy technique into multi-party learning to get rid of privacy threats, and offers sharper quantitative results. We further design a dynamic privacy budget allocating strategy to alleviate the high accumulation of total privacy budgets and provide better privacy guarantees, without compromising the model’s utility. We inject more noise into model parameters in the early stages of model training and gradually reduce the volume of noise as the direction of gradient descent becomes more accurate. Theoretical analysis and extensive experiments on benchmark datasets validated that our approach could effectively improve the model’s performance with less privacy loss.

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40

Ziegler, Joceline, Bjarne Pfitzner, Heinrich Schulz, Axel Saalbach y Bert Arnrich. "Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data". Sensors 22, n.º 14 (11 de julio de 2022): 5195. http://dx.doi.org/10.3390/s22145195.

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Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.94 for ε=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.

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41

Nolan, Jim y Jacqueline Morgan. "Privacy". Medical Journal of Australia 142, n.º 5 (marzo de 1985): 332. http://dx.doi.org/10.5694/j.1326-5377.1985.tb113396.x.

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42

Dent, M. H. "Privacy". Medical Journal of Australia 142, n.º 2 (enero de 1985): 168. http://dx.doi.org/10.5694/j.1326-5377.1985.tb133089.x.

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43

Camp, L. J. "Privacy". ACM SIGCAS Computers and Society 24, n.º 3 (septiembre de 1994): 8–15. http://dx.doi.org/10.1145/191634.191637.

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44

Williams, Michael E. "Privacy". New Blackfriars 75, n.º 878 (enero de 1994): 65–72. http://dx.doi.org/10.1111/j.1741-2005.1994.tb01467.x.

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45

Zuern, John David. "Privacy". a/b: Auto/Biography Studies 32, n.º 2 (25 de abril de 2017): 267–70. http://dx.doi.org/10.1080/08989575.2017.1288034.

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46

Kemp, Randy y Adam D. Moore. "Privacy". Library Hi Tech 25, n.º 1 (13 de marzo de 2007): 58–78. http://dx.doi.org/10.1108/07378830710735867.

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47

Moore, Barrington. "Privacy". Society 22, n.º 4 (mayo de 1985): 17–27. http://dx.doi.org/10.1007/bf02701909.

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48

Brown, Roger. "Privacy". Computer Law & Security Review 3, n.º 3 (septiembre de 1987): 8–11. http://dx.doi.org/10.1016/0267-3649(87)90018-5.

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49

Greenleaf, Graham. "Privacy". Computer Law & Security Review 3, n.º 6 (marzo de 1988): 6–8. http://dx.doi.org/10.1016/0267-3649(88)90128-8.

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50

Culver, Charles, James Moor, William Duerfeldt, Marshall Kapp y Mark Sullivan. "Privacy". Professional Ethics, A Multidisciplinary Journal 3, n.º 3 (1994): 3–25. http://dx.doi.org/10.5840/profethics199433/413.

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