The increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data. Therefore, it is important to develop privacy-preserving techniques that support publishing of really anonymous data, without altering the analysis results significantly. In this paper we propose to apply the Privacy-by-design paradigm for designing a (...) technological framework to counter the threats of undesirable, unlawful effects of privacy violation on sequence data, without obstructing the knowledge discovery opportunities of data mining technologies. First, we introduce a k-anonymity framework for sequence data, by defining the sequence linking attack model and its associated countermeasure, a k-anonymity notion for sequence datasets, which provides a formal protection against the attack. Second, we instantiate this framework and provide a specific method for constructing the k-anonymous version of a sequence dataset, which preserves the results of sequential pattern mining, together with several basic statistics and other analytical properties of the original data, including the clustering structure. A comprehensive experimental study on realistic datasets of process-logs, web-logs and GPS tracks is carried out, which empirically shows how, in our proposed method, the protection of privacy meets analytical utility. (shrink)

With the proliferation of networked electronic communication came daunting capabilities to collect, process, combine and store data, resulting in hitherto unseen transformational pressure on the concepts of trust, security and privacy as we know them. The Future Internet will bring about a world where real life will integrate physical and digital life. Technology development for data linking and mining, together with unseen data collection, will lead to unwarranted access to personal data, and hence, privacy intrusion. Trust and (...) identity lie at the basis of many human interactions and transactions, and societies have developed legitimate concern for privacy being essential for freedom and creativity. The burgeoning development of the Information Society, particularly during the past fifteen years, transcended the societal readiness to respond to the transformational change evoked by ICT. We have reached the eleventh hour for the preservation of trust and privacy as elements that can be transposed into our digital future. Europe has been at the forefront in recognizing the importance of privacy protection in relation to digital data, witness the advanced European legislation in this domain. The European Commission recognizes that appropriate measures need to combine technology development with legal means, user awareness and tools supporting data controllers to comply with law in an accountable and transparent way, and that empower users with a controlling stake in managing their personal data. Activities are underway at many levels. European RTD programmes play their role in supporting research in trustworthy ICT, privacy enhancing technologies, privacy-by-design in service layers as well as in networks, enabling technologies such as cryptography, and in generalized frameworks for trust and privacy-protective identity management. (shrink)

With the proliferation of networked electronic communication came daunting capabilities to collect, process, combine and store data, resulting in hitherto unseen transformational pressure on the concepts of trust, security and privacy as we know them. The Future Internet will bring about a world where real life will integrate physical and digital life. Technology development for data linking and mining, together with unseen data collection, will lead to unwarranted access to personal data, and hence, privacy intrusion. Trust and (...) identity lie at the basis of many human interactions and transactions, and societies have developed legitimate concern for privacy being essential for freedom and creativity. The burgeoning development of the Information Society, particularly during the past fifteen years, transcended the societal readiness to respond to the transformational change evoked by ICT. We have reached the eleventh hour for the preservation of trust and privacy as elements that can be transposed into our digital future. Europe has been at the forefront in recognizing the importance of privacy protection in relation to digital data, witness the advanced European legislation in this domain. The European Commission recognizes that appropriate measures need to combine technology development with legal means, user awareness and tools supporting data controllers to comply with law in an accountable and transparent way, and that empower users with a controlling stake in managing their personal data. Activities are underway at many levels. European RTD programmes play their role in supporting research in trustworthy ICT, privacy enhancing technologies, privacy-by-design in service layers as well as in networks, enabling technologies such as cryptography, and in generalized frameworks for trust and privacy-protective identity management. (shrink)

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, (...) such as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm. (shrink)

With the increasing volume of web services in the cloud environment, Collaborative Filtering- based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other (...) cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRecSimHash, is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation. (shrink)

The development and application of artificial intelligence (AI) technology has raised many concerns about privacy violations in the public. Thus, privacy-preserving computation technologies (PPCTs) have been developed, and it is expected that these new privacy protection technologies can solve the current privacy problems. By not directly using raw data provided by users, PPCTs claim to protect privacy in a better way than their predecessors. They still have technical limitations, and considerable research has treated PPCTs (...) as a privacy-protecting tool and focused on possible technical improvements. In this article, we argue that PPCTs cannot effectively protect privacy in the narrow sense due to their technical limitations. Moreover, although these shortcomings could be remedied, they still fall into a paradox because their aim is to protect privacy, but they could reveal private information from users. This paradox of privacy protection not only aggravates the social impact arising from AI privacy issues but may also make the current privacy protection lose its meaning, resulting in a situation in which privacy protection is useless. (shrink)

We present the design of a secure and privacy preserving e-petition system that we have implemented as a proof-of-concept demonstrator. We use the Belgian e-ID card as source of authentication, and then proceed to issue an anonymous credential that is used to sign petitions. Our system ensures that duplicate signatures are detectable, while preserving the anonymity of petition signers. We analyze the privacy and security requirements of our application, present an overview of its architecture, and discuss (...) the applicability of data protection legislation to our system. (shrink)

In this paper, we study the problem of protecting privacy in recommender systems. We focus on protecting the items rated by users and propose a novel privacy-preserving matrix factorization algorithm. In our algorithm, the user will submit a fake gradient to make the central server not able to distinguish which items are selected by the user. We make the Kullback–Leibler distance between the real and fake gradient distributions to be small thus hard to be distinguished. Using theories (...) and experiments, we show that our algorithm can be reduced to a time-delay SGD, which can be proved to have a good convergence so that the accuracy will not decline. Our algorithm achieves a good tradeoff between the privacy and accuracy. (shrink)

With the development of mobile communication technology, location-based services are booming prosperously. Meanwhile privacy protection has become the main obstacle for the further development of LBS. The k-nearest neighbor search is one of the most common types of LBS. In this paper, we propose an efficient private circular query protocol with high accuracy rate and low computation and communication cost. We adopt the Moore curve to convert two-dimensional spatial data into one-dimensional sequence and encrypt the points of interest information (...) with the Brakerski-Gentry-Vaikuntanathan homomorphic encryption scheme for privacy-preserving. The proposed scheme performs the secret circular shift of the encrypted POIs information to hide the location of the user without a trusted third party. To reduce the computation and communication cost, we dynamically divide the table of the POIs information according to the value of k. Experiments show that the proposed scheme provides high accuracy query results while maintaining low computation and communication cost. (shrink)

This paper addresses the always online behavior of the data owner in proxy re- encryption schemes for re-encryption keys issuing. We extend and adapt multi-authority ciphertext policy attribute based encryption techniques to type-based proxy re-encryption to build our solution. As a result, user authentication and user authorization are moved to the cloud server which does not require further interaction with the data owner, data owner and data users identities are hidden from the cloud server, and re-encryption keys are only issued (...) to legitimate users. An in depth analysis shows that our scheme is secure, flexible and efficient for mobile cloud computing. (shrink)

Differential privacy (DP) aims to confer data processing systems with inherent privacy guarantees, offering strong protections for personal data. But DP’s approach to privacy carries with it certain assumptions about how mathematical abstractions will be translated into real-world systems, which—if left unexamined and unrealized in practice—could function to shield data collectors from liability and criticism, rather than substantively protect data subjects from privacy harms. This article investigates these assumptions and discusses their implications for using DP to (...) govern data-driven systems. In Parts 1 and 2, we introduce DP as, on one hand, a mathematical framework and, on the other hand, a kind of real-world sociotechnical system, using a hypothetical case study to illustrate how the two can diverge. In Parts 3 and 4, we discuss the way DP frames privacy loss, data processing interventions, and data subject participation, arguing it could exacerbate existing problems in privacy regulation. In part 5, we conclude with a discussion of DP’s potential interactions with the endogeneity of privacy law, and we propose principles for best governing DP systems. In making such assumptions and their consequences explicit, we hope to help DP succeed at realizing its promise for better substantive privacy protections. (shrink)

Personal medical information is now bought and sold on the open market. Companies use it to make hiring and firing decisions and to identify customers for new products. The justification for providing such access to medical information is that doing so benefits the public by securing public safety, controlling costs, and supporting medical research. And individuals have supposedly consented to it. But we can achieve the common goods while better protecting privacy by making institutional changes in the way information (...) is maintained and protected. (shrink)

Privacy concerns involving data mining are examined in terms of four questions: What exactly is data mining? How does data mining raise concerns for personal privacy? How do privacy concerns raised by data mining differ from those concerns introduced by traditional information-retrieval techniques in computer databases? How do privacy concerns raised by mining personal data from the Internet differ from those concerns introduced by mining such data from data warehouses? It (...) is argued that the practice of using data-mining techniques, whether on the Internet or in data warehouses, to gain information about persons raises privacy concerns that go beyond concerns introduced in traditional information-retrieval techniques in computer databases and are not covered by present data-protection guidelines and privacy laws. (shrink)

Recently, there has been a heavy debate in the US about the government’s use of data mining in its fight against terrorism. Privacy concerns in fact led the Congress to terminate the funding of TIA, a program for advanced information technology to be used in the combat of terrorism. The arguments put forward in this debate, more specifically those found in the main report and minority report by the TAPAC established by the Secretary of Defense to examine the (...) TIA issue, will be analysed to trace the deeper roots of this controversy. This analysis will in turn be used as a test case to examine the adequacy of the usual theoretical frameworks for these kinds of issues, in particular the notion of privacy. Whereas the dominant theoretical framing of the notion of privacy turns around access to information, most of the core arguments in the debate do not fit in this kind of framework. The basic disagreements in the controversy are not about mere access, they involve both access and use. Furthermore, whereas the issue of access by itself refers to a more or less static situation, the real disagreements much more concern the organisational dynamics of the use of information, the mechanisms in the organisation that control these dynamics, and the awareness present within the organisation of the ‘social risks’ these dynamics represent. The bottom line question is whether the assessment of these gives sufficient reason for trust. (shrink)

This paper explores the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of social network sites users. It applies a data mining algorithm to a real dataset to provide empirically-based evidence of the ease with which characteristics about the SNS users can be discovered and used in a way that could invade their privacy. One major contribution of this article is the use of the decision (...) forest data mining algorithm to the context of SNS, which does not only build a decision tree but rather a forest allowing the exploration of more logic rules from a dataset. One logic rule that SysFor built in this study, for example, revealed that anyone having a profile picture showing just the face or a picture showing a family is less likely to be lonely. Another contribution of this article is the discussion of the implications of the data mining problem for governments, businesses, developers and the SNS users themselves. (shrink)

Providing Single Sign-On (SSO) between service providers and enabling service providers to share user personal attributes are critical for both users to benefit from a seamless access to their services, and service providers to realize new business opportunities. Today, however, the users have several independent, partial identities spread over different service providers. Providing SSO and attribute sharing requires that links (federations) are established between (partial) identities. In SAML 2.0 (Maler et al. 2003), the links between identities are stored and managed (...) at the network side by the identity providers (network-side identity federation). This model prevents the service providers from mass-correlating the partial identities they have, but the users must fully trust the identity providers. In this paper, we propose a complementary approach where the users have a full control of the links between their partial identities. It is a client-side identity federation approach, which relies on the introduction of a new cryptographic tool, called invariable partially blind signature scheme, that may be of independent interest. (shrink)

This essay examines issues involving personal privacy and informed consent that arise at the intersection of information and communication technology and population genomics research. I begin by briefly examining the ethical, legal, and social implications program requirements that were established to guide researchers working on the Human Genome Project. Next I consider a case illustration involving deCODE Genetics, a privately owned genetics company in Iceland, which raises some ethical concerns that are not clearly addressed in the current ELSI guidelines. (...) The deCODE case also illustrates some ways in which an ICT technique known as data mining has both aided and posed special challenges for researchers working in the field of population genomics. On the one hand, data-mining tools have greatly assisted researchers in mapping the human genome and in identifying certain “disease genes” common in specific populations. On the other hand, this technology has significantly threatened the privacy of research subjects participating in population genomics studies, who may, unwittingly, contribute to the construction of new groups that put those subjects at risk for discrimination and stigmatization. In the final section of this paper I examine some ways in which the use of data mining in the context of population genomics research poses a critical challenge for the principle of informed consent, which traditionally has played a central role in protecting the privacy interests of research subjects participating in epidemiological studies. (shrink)

In earlier work, I have expressed scepticism about privacy-based criticisms of bulk collection for counter-terrorism ( Sorell 2018 ). But even if these criticisms are accepted, is bulk collection nonetheless legitimate on balance – because of its operational utility for the security services, and the overriding importance of the purposes that the security services serve? David Anderson’s report of the Bulk Powers review in the United Kingdom suggests as much, provided bulk collection complies with strong legal safeguards ( (...) Anderson 2016 ). I think it is hard to mount a uniformly compelling operational utility argument, because purposes other than counter-terrorism are pursued by the security services with the help of bulk collection. For example, the Intelligence Services Act 1994, section 1(2) says that apart from the interests of national security and the prevention and detection of serious crime, the Secret Intelligence Service may act “in the interests of the economic well-being of the United Kingdom.”The phrase “interests of the economic well-being of the United Kingdom” is opento a disturbingly wide range of interpretations, and it is doubtful that these interests justify bulk collection even when relevant “economic interests” are confined to those “also relevant to the interests of national security”, as required by the Investigatory Powers Act (2016) section 204 (3a). (shrink)

Education at all levels is increasingly augmented and enhanced by data mining and analytics, catalysed by the growing prevalence of automated distance learning. With an unprecedented capacity to scale both horizontally and vertically, data mining and analytics are set to be a transformative part of the future of education. We reflect on the assumptions behind data mining and the potential consequences of learning analytics, with reference to an issue brief prepared for the U.S. Department of Education entitled (...) Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics. We argue that the associated gains conceal subtle, but important risks. Data-ism, an underpinning paradigm, assigns unjustified veracity to data-driven science and the application of personalised analytics may compromise individual privacy, agency and inventiveness. This holds serious ethical implications, particularly when considering the impact on minors, rendering wholesale adoption premature. (shrink)

Discovering high-utility itemsets (HUIs) consists of finding sets of items that yield a high profit in customer transaction databases. An important limitation of traditional high-utility itemset mining (HUIM) is that only the utility measure is used for assessing the interestingness of patterns. This leads to finding several itemsets that have a high profit but contain items that are weakly correlated. To address this issue, this paper proposes to integrate the concept of correlation in HUIM to find (...) profitable itemsets that are highly correlated, using the all-confidence and bond measures. An efficient algorithm named FCHM (fast correlated high-utility itemset miner) is proposed to efficiently discover correlated high-utility itemsets (CHIs). Two versions of the algorithm are proposed: FCHM|$_{all\text{-}confidence}$| and FCHM|$_{bond}$|⁠, which are based on the all-confidence and bond measures, respectively. An experimental evaluation was done using four real-life benchmark datasets from the HUIM literature: mushroom, retail, kosarak and foodmart. Results show that FCHM is efficient and can prune a huge amount of weakly CHIs. (shrink)

Distributed programming paradigms such as MapReduce and Spark have alleviated sequential bottleneck while mining of massive transaction databases. Of significant importance is mining High Utility Itemset that incorporates the revenue of the items purchased in a transaction. Although a few algorithms to mine HUIs in the distributed environment exist, workload skew and data transfer overhead due to shuffling operations remain major issues. In the current study, Parallel Utility Computation algorithm has been proposed with novel grouping and (...) load balancing strategies for an efficient mining of HUIs in a distributed environment. To group the items, Transaction Weighted Utility values as a degree of transaction similarity is employed. Subsequently, these groups are assigned to the nodes across the cluster by taking into account the mining load due to the items in the group. Experimental evaluation on real and synthetic datasets demonstrate that PUC with TWU grouping in conjunction with load balancing converges mining faster. Due to reduced data transfer, and load balancing-based assignment strategy, PUC outperforms different grouping strategies and random assignment of groups across the cluster. Also, PUC is shown to be faster than PHUI-Growth algorithm with a promising speedup. (shrink)

Historic preservation, which is a vital act for conveying people’s understanding of the past, such as events, ideas, and places to the future, allows people to preserve history for future generations. Additionally, since the historic properties are currently concentrated in urban areas, an urban-oriented approach will contribute to the issue. Hence, public awareness is a key factor that paves the way for this conservation. Public history, a history with a public audience and special methods of representation, can serve society in (...) this regard. Historical video games, which get their game objectives from the past, can be considered a form of public history, raising people’s awareness and letting them gain historical experience. While plenty of studies focused on this content with various approaches, this study takes the topic under consideration with a player-oriented focus. The research uses the Steam video game digital distribution service database to explore the existing historical games based on their genre, player scale, dimension, VR compatibility attributes, and player rating. The database includes 1,148 games published between 2002 and 2022, filtered as historical by Steam tags and sorted by player review. For the data collection, the study uses the Octoparse web scraping tool for data extraction, and for the data analysis, it uses RapidMiner Studio data mining software. The study aims to provide a framework applicable to the development of commercially playable historical video games contributing to heritage preservation by raising public awareness in the context of public history. (shrink)

"Data mining is not an invasion of privacy because access to data is only by machines, not by people": this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the analysis (...) will have to be made either by humans or by machines that understand. It concludes that the current data mining violates the right to privacy and should be subject to the standard legal constraints for access to private information by people. (shrink)

The present study examines certain challenges that KDD (Knowledge Discovery in Databases) in general and data mining in particular pose for normative privacy and public policy. In an earlier work (see Tavani, 1999), I argued that certain applications of data-mining technology involving the manipulation of personal data raise special privacy concerns. Whereas the main purpose of the earlier essay was to show what those specific privacy concerns are and to describe how exactly those concerns have (...) been introduced by the use of certain KDD and data-mining techniques, the present study questions whether the use of those techniques necessarily violates the privacy of individuals. This question is considered vis-à-vis a recent theory of privacy advanced by James Moor (1997). The implications of that privacy theory for a data-mining policy are also considered. (shrink)

:This article examines privacy threats arising from the use of data mining by private Australian health insurance companies. Qualitative interviews were conducted with key experts, and Australian governmental and nongovernmental websites relevant to private health insurance were searched. Using Rationale, a critical thinking tool, the themes and considerations elicited through this empirical approach were developed into an argument about the use of data mining by private health insurance companies. The argument is followed by an ethical analysis guided (...) by classical philosophical theories—utilitarianism, Mill’s harm principle, Kant’s deontological theory, and Helen Nissenbaum’s contextual integrity framework. Both the argument and the ethical analysis find the use of data mining by private health insurance companies in Australia to be unethical. Although private health insurance companies in Australia cannot use data mining for risk rating to cherry-pick customers and cannot use customers’ personal information for unintended purposes, this article nonetheless concludes that the secondary use of customers’ personal information and the absence of customers’ consent still suggest that the use of data mining by private health insurance companies is wrong. (shrink)

Intelligent Transportation Systems are expected to automate how parking slots are booked by trucks. The intrinsic dynamic nature of this problem, the need of explanations and the inclusion of private data justify an agent-based solution. Agents solving this problem act with a Believe Desire Intentions reasoning, and are implemented with JASON. Privacy of trucks becomes protected sharing a list of parkings ordered by preference. Furthermore, the process of assigning parking slots takes into account legal requirements on breaks and driving (...) time limits. Finally, the agent simulations use the distances, the number of trucks and parkings corresponding to the proportions of the current European Union data. The performance of the proposed solution is tested in these simulations with three different distances against an alternative with complete knowledge. The difference in efficiency, the number of illegal breaks and the traveled distances are measured in them. Comparing the results, we can conclude that the nonprivate alternative is slightly better in performance while both alternatives do not produce illegal breaks. In this way the simulations show that the proposed privacy protection does not impose a relevant handicap in efficiency. (shrink)

At the beginning of the COVID-19 pandemic, high hopes were placed on digital contact tracing. Digital contact tracing apps can now be downloaded in many countries, but as further waves of COVID-19 tear through much of the northern hemisphere, these apps are playing a less important role in interrupting chains of infection than anticipated. We argue that one of the reasons for this is that most countries have opted for decentralised apps, which cannot provide a means of rapidly informing users (...) of likely infections while avoiding too many false positive reports. Centralised apps, in contrast, have the potential to do this. But policy making was influenced by public debates about the right app configuration, which have tended to focus heavily on privacy, and are driven by the assumption that decentralised apps are “privacy preserving by design”. We show that both types of apps are in fact vulnerable to privacy breaches, and, drawing on principles from safety engineering and risk analysis, compare the risks of centralised and decentralised systems along two dimensions, namely the probability of possible breaches and their severity. We conclude that a centralised app may in fact minimise overall ethical risk, and contend that we must reassess our approach to digital contact tracing, and should, more generally, be cautious about a myopic focus on privacy when conducting ethical assessments of data technologies. (shrink)

Privacy, Due process and the Computational Turn: The Philosophy of Law Meets the Philosophy of Technology engages with the rapidly developing computational aspects of our world including data mining, behavioural advertising, iGovernment, profiling for intelligence, customer relationship management, smart search engines, personalized news feeds, and so on in order to consider their implications for the assumptions on which our legal framework has been built. The contributions to this volume focus on the issue of privacy, which is often (...) equated with data privacy and data security, location privacy, anonymity, pseudonymity, unobservability, and unlinkability. Here, however, the extent to which predictive and other types of data analytics operate in ways that may or may not violate privacy is rigorously taken up, both technologically and legally, in order to open up new possibilities for considering, and contesting, how we are increasingly being correlated and categorizedin relationship with due process – the right to contest how the profiling systems are categorizing and deciding about us. (shrink)

The processing of sensitive information in the health field is subject to rigorous standards that guarantee the protection of information confidentiality. Recently, the Italian Data Protection Authority (Garante per la Protezione dei Dati Personali) stated their formal opinion on a standard procedure in dental offices involving the submission of a questionnaire that includes the patient's health status. HIV infection status is included on the form. The Authority has stated that all health data collection must be in accordance with the current (...) Italian normative framework for personal data protection and respect the patient's freedom. This freedom allows the patient to decide, in a conscious and responsible way, whether to share health information with health personnel without experiencing any prejudice in the provision of healthcare requested. Moreover, data collection must be relevant and cannot exceed the principles of treatment goals with reference to the specific care of the concerned person. However, the need for recording information regarding HIV infection at the first appointment, regardless of the clinical intervention or therapeutic plan that needs to be conducted, should not alter the standard protection measures of the healthcare staff. In fact, these measures are adopted for every patient. (shrink)

In the last chapter of The Ethics of Liberty, Rothbard discusses his theory of strategy for liberty, and recommends tools such as education that libertarians can lean on to attain the highest political goal of freedom. Building on Rothbard’s shoulders, the main thesis of this paper is that an effective theory of strategy for liberty cannot dispense with privacy, which needs to be understood as a condition for the enjoyment of liberty and not as a right per se. In (...) the first section, the discussion is framed in the context of natural rights libertarianism. Then, a metaphysical taxonomy of property is provided, which articulates the functioning of property rights and privacy in the realm of the body and of the mind, in the realm of alienable goods and services, and in the realm of information. The third section deals with the war on privacy that is raging nowadays; not coincidentally, the ultimate enemy of this war is private property. The last part of the paper contends that Rothbard is correct in reducing privacy rights to property rights, but this doesn’t mean that privacy has no place in libertarian thought; on the contrary, privacy is one of the main conditions for the defense and preservation of property rights, and, in the case of information, property cannot even exist without it. If these theses are true, libertarians need to find a proper place for privacy in their theory of strategy for liberty. (shrink)

In light of technology that may reveal the content of a person’s innermost thoughts, I address the question of whether there is a right to ‘brain privacy’—a right not to have one’s inner thoughts revealed to others–even if exposing these thoughts might be beneficial to society. I draw on a conception of privacy as the ability to control who has access to information about oneself and to an account that connects one’s interest in privacy to one’s interests (...) in autonomy and associated reputational interests, and preserving one’s dignity. Focusing on the controversial case of Gilberto Valle, known as the ‘cannibal cop’, who faced legal punishment and moral reproach for deeply disturbing thoughts of doing violence to women, thoughts he claimed were mere fantasies, I argue that Valle has a right to brain privacy if he has a legitimate privacy interest that is not outweighed by competing societal interests in avoiding harm. Our weighing of competing privacy and societal interests will depend on the magnitude of the privacy interests in autonomy and dignity, and on how reliable the technology used to expose inner thoughts is in predicting future harmful behavior and identifying true threats. (shrink)

As libraries begin to embrace Web 2.0 technologies to serve patrons—ushering in the era of Library 2.0—unique dilemmas arise in the realm of information ethics, especially regarding patron privacy. The norms of Web 2.0 promote the open sharing of information—often personal information—and the design of many Library 2.0 services capitalize on access to patron information and might require additional tracking, collection, and aggregation of patron activities. Thus, embracing Library 2.0 potentially threatens the traditional ethics of librarianship, where protecting patron (...) privacy and intellectual freedom has been held paramount. The question is not whether libraries will move toward Library 2.0, but how they will do it, and whether they can preserve the contextual integrity of patron privacy and maintain their professional librarian ethic, while also providing enhanced services to their patrons. This article will provide an ethical examination of the emergence of new Library 2.0 tools and technologies in relation to existing ethical norms of information flow within the library context. By doing so, librarians and information professionals will be better situated to avoid—or at least renegotiate—the impending Faustian bargain regarding patron privacy in the "2.0" era. (shrink)

Since the outbreak of COVID-19, governments have turned their attention to digital contact tracing. In many countries, public debate has focused on the risks this technology poses to privacy, with advocates and experts sounding alarm bells about surveillance and mission creep reminiscent of the post 9/11 era. Yet, when Apple and Google launched their contact tracing API in April 2020, some of the world’s leading privacy experts applauded this initiative for its privacy-preserving technical specifications. In an (...) interesting twist, the tech giants came to be portrayed as greater champions of privacy than some democratic governments. This article proposes to view the Apple/Google API in terms of a broader phenomenon whereby tech corporations are encroaching into ever new spheres of social life. From this perspective, the advantage these actors have accrued in the sphere of the production of digital goods provides them with access to the spheres of health and medicine, and more worrisome, to the sphere of politics. These sphere transgressions raise numerous risks that are not captured by the focus on privacy harms. Namely, a crowding out of essential spherical expertise, new dependencies on corporate actors for the delivery of essential, public goods, the shaping of public policy by non-representative, private actors and ultimately, the accumulation of decision-making power across multiple spheres. While privacy is certainly an important value, its centrality in the debate on digital contact tracing may blind us to these broader societal harms and unwittingly pave the way for ever more sphere transgressions. (shrink)