Relevant bibliographies by topics / Privacy

Academic literature on the topic 'Privacy'

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Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Privacy"

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Serwin, Andrew. "Privacy 3.0-The Principle of Proportionality." University of Michigan Journal of Law Reform, no. 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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Cohen, Julie E. "Turning Privacy Inside Out." Theoretical Inquiries in Law 20, no. 1 (March 16, 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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Lemley, Mark. "Privacy, Property, and Publicity." Michigan Law Review, no. 117.6 (2019): 1153. http://dx.doi.org/10.36644/mlr.117.6.privacy.

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Culshaw, Helen. "Private sector libraries and privacy." ANZTLA EJournal, no. 49 (April 29, 2019): 10–13. http://dx.doi.org/10.31046/anztla.v0i49.1196.

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Munro, Moira, and Ruth Madigan. "Privacy in the private sphere." Housing Studies 8, no. 1 (January 1993): 29–45. http://dx.doi.org/10.1080/02673039308720748.

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Etzioni, Amitai. "Privacy and the private realm." Innovation: The European Journal of Social Science Research 25, no. 1 (March 2012): 57–66. http://dx.doi.org/10.1080/13511610.2012.655574.

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Darr, Kurt. "Privacy—Private Lives, Public Lives." Hospital Topics 81, no. 3 (January 2003): 29–32. http://dx.doi.org/10.1080/00185860309598025.

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Bläser, Markus, Andreas Jakoby, Maciej Liśkiewicz, and Bodo Manthey. "Privacy in Non-private Environments." Theory of Computing Systems 48, no. 1 (October 16, 2009): 211–45. http://dx.doi.org/10.1007/s00224-009-9243-1.

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Nissim, Kobbi, and Alexandra Wood. "Is privacy privacy ?" Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 376, no. 2128 (August 6, 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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Sari, Putri Widya. "Pengungkapan Diri dalam Perseteruan Youtuber Kembar Tasya Farasya dan Tasyi Athasyia." Jurnal Komunikasi Nusantara 5, no. 1 (June 1, 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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Dissertations / Theses on the topic "Privacy"

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Loesing, Karsten. "Privacy-enhancing technologies for private services." Bamberg Univ. of Bamberg Press, 2009. http://d-nb.info/994593937/34.

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RICCI, STEFANO. "Il global privacy standard: i modelli di tutela della privacy." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/44620.

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Questo lavoro propone un approccio alternativo per la protezione dei dati personali in termini di confidenzialità: l'argomento principale è che la confidenzialità dovrebbe avere più rilevanza nell'ambito della data protection, perché illumina il rapporto tra coloro che hanno le informazioni e coloro ai quali i dati si riferiscono. In realtà, il deficit di comprensione nelle leggi sulla protezione dei dati è dovuto ad un equivoco riguardo la centralità di un diritto individualista e generale qual è la privacy intesa come scudo contro le ingerenze arbitrarie, piuttosto che la privacy intesa come protezione di rapporti fiduciari. Concettualizzare la protezione dei dati attraverso la confidenzialità serve a sottolineare le violazioni di doveri fiduciari piuttosto che la lesione di diritti fondamentali. Il Global Privacy Standard (GPS) e le Fair Information Practices (FIPS) mostrano chiaramente come la data protection possa essere impostata in termini di confidenzialità. Da questo punto di vista, il GPS è da intendersi non come la creazione di un diritto generale alla privacy in base alla natura privata di quelle informazioni, quanto piuttosto come il tentativo di regolare la circolazione dei dati personali in base al contesto in cui tali informazioni vengono scambiate.
This work proposes an alternative account of data protection in terms of confidentiality: the argument is that confidentiality should have more relevance because it lights up the relationship between those who have information and those to whom the information relates. In fact, I argue that the deficit of understanding in data protection laws is due to an equivocal centrality of the need to safeguard an individualistic and general right of privacy as shield against arbitrary interference instead of a need to protect relationships of trust. Conceptualising data protection through confidentiality serves to point out the breaches of fiduciary duties. In contrast to the classic approach of privacy and data protection, confidentiality focuses on relationships rather than individuals because, far from a right to be let alone, confidentiality is based on the rules of trust within relationships: it is adequate to describe a breach of privacy through confidentiality as a breach of an implicit clause of a fiduciary relationship existing between the data controller (the confidant) and the data subject (the confider). Global Privacy Standard (GPS) and Fair Information Practises (FIPs) show a clearly delineated ground for personal data protection in the form of confidentiality. If GPS are understood not as creating a general right of privacy of personal data, but as carrying out the extremely different duty of confidentiality with respect to data protection, GPS can be seen not as attempting to regulate personal data based on the private nature of that information, but only establish the framework in which such information is exchanged. Data protection should therefore be separated from privacy and should put into a legal frame of confidentiality, so that personal data could be better protected.
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Foerster, Marian. "WWW Privacy - P3P Platform of Privacy Preferencers." Universitätsbibliothek Chemnitz, 2000. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200000598.

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Gemeinsamer Workshop von Universitaetsrechenzentrum und Professur Rechnernetze und verteilte Systeme (Fakultaet fuer Informatik) der TU Chemnitz. Workshop-Thema: Infrastruktur der ¨Digitalen Universitaet¨ WWW Privacy - P3P Platform of Privacy Preferencers Der Vortrag soll einen Einblick in das z.Zt. noch in der Entwicklung stehenden Protokolls P3P des W3C geben. Dabei wird das Grundprinzip von P3P, einige technische Realisierungsmoeglichkeiten sowie ein Demo-Einkaufssystem vorgestellt.
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Purandare, Darshan. "ENHANCING MESSAGE PRIVACY IN WIRED EQUIVALENT PRIVACY." Master's thesis, University of Central Florida, 2005. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2998.

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The 802.11 standard defines the Wired Equivalent Privacy (WEP) and encapsulation of data frames. It is intended to provide data privacy to the level of a wired network. WEP suffered threat of attacks from hackers owing to certain security shortcomings in the WEP protocol. Lately, many new protocols like WiFi Protected Access (WPA), WPA2, Robust Secure Network (RSN) and 802.11i have come into being, yet their implementation is fairly limited. Despite its shortcomings one cannot undermine the importance of WEP as it still remains the most widely used system and we chose to address certain security issues and propose some modifications to make it more secure. In this thesis we have proposed a modification to the existing WEP protocol to make it more secure. We achieve Message Privacy by ensuring that the encryption is not breached. The idea is to update the shared secret key frequently based on factors like network traffic and number of transmitted frames. We also develop an Initialization Vector (IV) avoidance algorithm that eliminates IV collision problem. The idea is to partition the IV bits among different wireless hosts in a predetermined manner unique to every node. We can use all possible 224 different IVs without making them predictable for an attacker. Our proposed algorithm eliminates the IV collision ensuring Message Privacy that further strengthens security of the existing WEP. We show that frequent rekeying thwarts all kinds of cryptanalytic attacks on the WEP.
M.S.
School of Computer Science
Engineering and Computer Science
Computer Science
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WITTE, NATHAN ALLAN. "PRIVACY: ARCHITECTURE IN SUPPORT OF PRIVACY REGULATION." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1053701814.

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Witte, Nathan. "Privacy architecture in support of privacy regulation /." Cincinnati, Ohio : University of Cincinnati, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=ucin1053701814.

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De, Montjoye Yves-Alexandre. "Computational privacy : towards privacy-conscientious uses of metadata." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101850.

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Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 111-127).
The breadcrumbs left behind by our technologies have the power to fundamentally transform the health and development of societies. Metadata about our whereabouts, social lives, preferences, and finances can be used for good but can also be abused. In this thesis, I show that the richness of today's datasets have rendered traditional data protections strategies outdated, requiring us to deeply rethink our approach. First, I show that the concept of anonymization, central to legal and technical data protection frameworks, does not scale. I introduce the concept of unicity to study the risks of re-identification of large-scale metadata datasets given p points. I then use unicity to show that four spatio-temporal points are enough to uniquely identify 95% of people in a mobile phone dataset and 90% of people in a credit card dataset. In both cases, I also show that traditional de-identification strategies such as data generalization are not sufficient to approach anonymity in modern high-dimensional datasets. Second, I argue that the second pillar of data protection, risk assessment, is similarly crumbling as data gets richer. I show, for instance, how standard mobile phone data-information on how and when somebody calls or texts-can be used to predict personality traits up to 1.7 times better than random. The risk of inference in big data will render comprehensive risks assessments increasingly difficult and, moving forward, potentially irrelevant as they will require evaluating what can be inferred now, and in the future, from rich data. However, this data has a great potential for good especially in developing countries. While it is highly unlikely that we will ever find a magic bullet or even a one-size- fits-all approach to data protection, there are ways that exist to use metadata in privacy-conscientious ways. I finish this thesis by discussing technical solutions (incl. privacy-through-security ones) which, when combined with legal and regulatory frameworks, provide a reasonable balance between the imperative of using this data and the legitimate concerns of the individual and society.
by Yves-Alexandre de Montjoye.
Ph. D.
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Sato, Keiko. "Privacy on the internet : Investigation into corporate privacy policy of Australian large private sector organisations on the internet." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2001. https://ro.ecu.edu.au/theses/1032.

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The popularity of the Internet has been dramatically increased over recent years. The rapid growth of this technology and its international use has made it almost impossible to regulate the internet. As a result, the Internet has certainly provided freedoms to people and it has led to some abusing systems. Privacy is one of the major issues in the development of Electronic Commerce using the Internet. As an enormous amount of personal information is transmitted to several hosts connecting to the Internet, the information can be accessed by both authorised and unauthorised people. Although it is certain that there are several existing problems of using the Internet for business activities, many organisations have already started using it. It is believed that the Internet provides efficiency and effectiveness for various activities Although much research has been described the business use of the Internet in many countries, these studies have not specifically investigated Australian organisations. Therefore, this research investigates the current use of the Internet by Australian organisations and their associated privacy policies, as a means of seeking their privacy concerns. Using a benchmark provided by Australian privacy commissioners, it evaluates their privacy policies to see how well they are established to protect privacy of users. The study utilises the top 100 Australian large private sector organisations as the sample. The current practice of the sample organisations on the Internet was observed by exploring their Web sites. Privacy policies were also collected from their Web sites. Moreover, a letter requesting corporate privacy policy was sent to each organisation that collects personal information on the Internet. The result showed that the majority of Australian organisations were using the Internet today, but a surprisingly few organisations showed their privacy policy on the Internet. Also, this research showed that many organisations did not actually have a corporate privacy policy. Many organisations are using the Internet without apparent concern for customers' privacy. The organisations proactively involved in the Internet Commerce are more concerned about security side of the Internet. Hence, they appear to believe that the technology itself protects information sent on the Internet. It has become clear that technology by itself does not provide the security needed for users of the Internet as unethical act of authorised parties could harm privacy of individuals. There is an argument that the Internet needs to be regulated. However, the process of international regulation on the Internet has not been started. Thus, it is ideal that organisations proactively protect clients' personal information accessible by the use of the Internet technology. This study looks at the methods of obtaining privacy of individuals and suggests the ideal conduct of organisations.
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Véliz, Carissa. "On privacy." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:afb31b0e-f022-48a6-b239-4c704cfd4484.

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This thesis concerns the ethics and political philosophy surrounding privacy. It investigates what privacy is, what is at stake in its loss, and how it relates to other rights and values. The first part sets the groundwork for the rest of the thesis. Chapter One delves into the origins of privacy. I argue that privacy is not a recent cultural product, but rather a need buried deep in our evolutionary and human history. The second part of the thesis is dedicated to conceptual issues. Chapter Two clarifies the relation between privacy and the public and private divide. I argue against the popular belief that privacy is an issue that belongs solely to the private sphere. Chapter Three reviews the most influential definitions of privacy that have been offered in the legal and philosophical literature, and points out some of their shortcomings and strengths. In Chapter Four, I develop my own definition of privacy as remaining personally unaccessed, as well as an account of the right to privacy as a right to a robustly demanding good. I also map out the moral significance of privacy perceptions, and privacy-related obligations. The third part of the dissertation concerns practical issues. Chapter Five inquires into the relationship between security and privacy. I argue that mass surveillance is a disproportionate, unnecessary, and ineffective response to the threat of terrorism. I also argue that encryption should be widely used, as it can curtail the mass surveillance of content and protect people without seriously obstructing criminal investigations. Chapter Six explores the relationship between privacy and transparency. I argue that transparency should sometimes be limited in the interest of privacy. Chapter Seven deals with the questions of whether we can lose privacy to computer algorithms, and whether decision-making algorithms can violate our right to privacy. I answer both questions in the negative, as algorithms are currently neither our peers nor moral agents responsible for their actions. The conclusion sketches some of the lessons learnt over the course of this investigation.
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Bruggen, Harry van der. "Patiënt, privaat en privacy de stoelgang als gezondheidswetenschappelijk probleem /." Lochem : Maastricht : De Tijdstroom ; University Library, Maastricht University [Host], 1991. http://arno.unimaas.nl/show.cgi?fid=5616.

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Books on the topic "Privacy"

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Akhtar, Salman, and Aisha Abbasi, eds. Privacy. New York : Routledge, 2019.: Routledge, 2019. http://dx.doi.org/10.4324/9780429202278.

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Filimowicz, Michael. Privacy. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003173335.

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Institute, Pennsylvania Bar. Privacy. [Mechanicsburg, Pa.] (5080 Ritter Rd., Mechanicsburg 17055-6903): Pennsylvania Bar Institute, 2005.

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Merino, Noël. Privacy. Detroit: Greenhaven Press, A part of Gale, Cengage Learning, 2014.

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Rodriguez, Kenneth. Privacy. Calabasas, CA: Center for Civic Education, 2001.

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Agostino, Clemente, ed. Privacy. Padova: CEDAM, 1999.

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Raymond, Wacks, ed. Privacy. New York, NY: New York University Press, 1993.

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Knopff, Rainer. Privacy. Calgary: The Calgary Institute for the Humanities, The University of Calgary, 1989.

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Rodriguez, Kenneth. Privacy. Calabasas, CA: Center for Civic Education, 2001.

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Scherer, Lauri S. Privacy. Detroit: Greenhaven Press, 2014.

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Book chapters on the topic "Privacy"

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Dannen, Chris, and Christopher White. "Privacy, Privacy, Privacy." In Beginning iOS Apps with Facebook and Twitter APIs, 9–14. Berkeley, CA: Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3543-9_2.

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Kanterian, Edward. "Privacy and Private Language." In A Companion to Wittgenstein, 443–64. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781118884607.ch28.

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Chinwah-Adegbola, Lovette. "Privacy." In Portable Health Records in a Mobile Society, 87–100. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19937-1_9.

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Levesque, Roger J. R. "Privacy." In Encyclopedia of Adolescence, 2161–64. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-1695-2_708.

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Posner, Richard A. "Privacy." In The New Palgrave Dictionary of Economics and the Law, 1503–8. London: Palgrave Macmillan UK, 2002. http://dx.doi.org/10.1007/978-1-349-74173-1_287.

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Hung, Patrick C. K., and Vivying S. Y. Cheng. "Privacy." In Encyclopedia of Database Systems, 1–2. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4899-7993-3_274-2.

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Rosenoer, Jonathan. "Privacy." In CyberLaw, 129–60. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4612-4064-8_4.

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Cofone, Ignacio N. "Privacy." In Encyclopedia of Law and Economics, 1644–48. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-7753-2_751.

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Cofone, Ignacio N. "Privacy." In Encyclopedia of Law and Economics, 1–4. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-7883-6_751-1.

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Schreck, Jörg. "Privacy." In Security and Privacy in User Modeling, 17–25. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0377-2_3.

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Conference papers on the topic "Privacy"

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Shani, Guy. "Advances and Challenges in Privacy Preserving Planning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/816.

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Collaborative privacy-preserving planning (CPPP) is a multi-agent planning task in which agents need to achieve a common set of goals without revealing certain private information. CPPP has gained attention in recent years as an important sub area of multi agent planning, presenting new challenges to the planning community. In this paper we describe recent advancements, and outline open problems and future directions in this field. We begin with describing different models of privacy, such as weak and strong privacy, agent privacy, and cardinality preserving privacy. We then discuss different solution approaches, focusing on the two prominent methods --- joint creation of a global coordination scheme first, followed by independent planning to extend the global scheme with private actions; and collaborative local planning where agents communicate information concerning their planning process. In both cases a heuristic is needed to guide the search process. We describe several adaptations of well known classical planning heuristic to CPPP, focusing on the difficulties in computing the heuristic without disclosing private information.
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Zhao, Wenhao, Shaoyang Song, and Chunlai Zhou. "Generate Synthetic Text Approximating the Private Distribution with Differential Privacy." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. California: International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/735.

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Due to the potential leakage of sensitive information in text, there is a societal call for feeding privacy-preserving text to model training. Recently, a lot of work showed that using synthetic text with differential privacy, rather than private text, can provide a strong privacy protection. However, achieving higher semantic similarity between synthetic and private text has not been thoroughly investigated. In this paper, we propose an approach that combines the iteratively optimized mindset from genetic algorithms to align the distribution of synthetic text with that of private text. Furthermore, not only does the final synthetic text meet the requirements of privacy protection, but also has a high level of quality. Through comparisons with various baselines on different datasets, we demonstrate that our synthetic text can closely match the utility of private text, while providing privacy protection standards robust enough to resist membership inference attacks from malicious users.
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"THE PRIVACY ADVOCATE (PRIVAD) - A Framework for Negotiating Individual Privacy Contracts." In 3rd International Conference on Web Information Systems and Technologies. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0001282100880095.

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Liu, Hongbin, Jinyuan Jia, and Neil Zhenqiang Gong. "On the Intrinsic Differential Privacy of Bagging." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/376.

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Differentially private machine learning trains models while protecting privacy of the sensitive training data. The key to obtain differentially private models is to introduce noise/randomness to the training process. In particular, existing differentially private machine learning methods add noise to the training data, the gradients, the loss function, and/or the model itself. Bagging, a popular ensemble learning framework, randomly creates some subsamples of the training data, trains a base model for each subsample using a base learner, and takes majority vote among the base models when making predictions. Bagging has intrinsic randomness in the training process as it randomly creates subsamples. Our major theoretical results show that such intrinsic randomness already makes Bagging differentially private without the needs of additional noise. Moreover, we prove that if no assumptions about the base learner are made, our derived privacy guarantees are tight. We empirically evaluate Bagging on MNIST and CIFAR10. Our experimental results demonstrate that Bagging achieves significantly higher accuracies than state-of-the-art differentially private machine learning methods with the same privacy budgets.
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Gu, He. "Towards Guaranteed Privacy in Stream Processing: Differential Privacy for Private Pattern Protection." In DEBS '23: 17th ACM International Conference on Distributed and Event-based Systems. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3583678.3603284.

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Gajera, Hardik, and Manik Lal Das. "Privc: Privacy Preserving Verifiable Computation." In 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE, 2020. http://dx.doi.org/10.1109/comsnets48256.2020.9027488.

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Inan, Ali, Murat Kantarcioglu, Gabriel Ghinita, and Elisa Bertino. "Private record matching using differential privacy." In the 13th International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1739041.1739059.

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A. Hayek, Frederic, Mirko Koscina, Pascal Lafourcade, and Charles Olivier-Anclin. "Generic Privacy Preserving Private Permissioned Blockchains." In SAC '23: 38th ACM/SIGAPP Symposium on Applied Computing. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3555776.3577735.

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Tran, Cuong, Ferdinando Fioretto, Pascal Van Hentenryck, and Zhiyan Yao. "Decision Making with Differential Privacy under a Fairness Lens." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/78.

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Many agencies release datasets and statistics about groups of individuals that are used as input to a number of critical decision processes. To conform with privacy and confidentiality requirements, these agencies are often required to release privacy-preserving versions of the data. This paper studies the release of differentially private datasets and analyzes their impact on some critical resource allocation tasks under a fairness perspective. The paper shows that, when the decisions take as input differentially private data, the noise added to achieve privacy disproportionately impacts some groups over others. The paper analyzes the reasons for these disproportionate impacts and proposes guidelines to mitigate these effects. The proposed approaches are evaluated on critical decision problems that use differentially private census data.
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Chaves, Iago, and Javam Machado. "Differentially Private Group-by Data Releasing Algorithm." In XXXIV Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sbbd.2019.8835.

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Privacy concerns are growing fast because of data protection regulations around the world. Many works have built private algorithms avoiding sensitive information leakage through data publication. Differential privacy, based on formal definitions, is a strong guarantee for individual privacy and the cutting edge for designing private algorithms. This work proposes a differentially private group-by algorithm for data publication under the exponential mechanism. Our method publishes data groups according to a specified attribute while maintaining the desired privacy level and trustworthy utility results.
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Reports on the topic "Privacy"

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Popovic, Ales, H. Jeff Smith, James Y. L. Thong, and Sunil Wattal. Information Privacy. MIS Quarterly, May 2017. http://dx.doi.org/10.25300/misq/2019/curations/05292017.

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Kakulla, Brittne. Topical Spotlight: Digital Privacy — Privacy Concerns Remain a Barrier. Washington, DC: AARP Research, August 2021. http://dx.doi.org/10.26419/res.00420.007.

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Goldfarb, Avi, and Catherine Tucker. Privacy and Innovation. Cambridge, MA: National Bureau of Economic Research, June 2011. http://dx.doi.org/10.3386/w17124.

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Bortzmeyer, S. DNS Privacy Considerations. RFC Editor, August 2015. http://dx.doi.org/10.17487/rfc7626.

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Wicinski, T., ed. DNS Privacy Considerations. RFC Editor, July 2021. http://dx.doi.org/10.17487/rfc9076.

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Lefkovitz, Naomi. NIST Privacy Framework:. Gaithersburg, MD: National Institute of Standards and Technology, 2024. http://dx.doi.org/10.6028/nist.cswp.10.may.

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Kissner, Lea, and Dawn Song. Privacy-Preserving Set Operations. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada457144.

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Seybold, Patricia. DuckDuckGo Respects Our Privacy. Boston, MA: Patricia Seybold Group, June 2013. http://dx.doi.org/10.1571/pp06-28-13cc.

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Jiang, S., S. Krishnan, and T. Mrugalski. Privacy Considerations for DHCP. RFC Editor, April 2016. http://dx.doi.org/10.17487/rfc7819.

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Krishnan, S., T. Mrugalski, and S. Jiang. Privacy Considerations for DHCPv6. RFC Editor, May 2016. http://dx.doi.org/10.17487/rfc7824.

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