PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference

Recent rapid development of sensor technology has allowed massive fine-grained time series (TS) data to be collected and set the foundation for the development of data-driven services and applications. During the process, data sharing is often involved to allow the third-party modelers to perform specific time series data mining (TSDM) tasks based on the need of data owner. The high resolution of TS brings new challenges in protecting privacy. While meaningful information in high-resolution TS shifts from concrete point values to local shape-based segments, numerous research have found that long shape-based patterns could contain more sensitive information and may potentially be extracted and misused by a malicious third party. However, the privacy issue for TS patterns is surprisingly seldom explored in privacy-preserving literature. In this work, we consider a new privacy-preserving problem: preventing malicious inference on long shape-based patterns while preserving short segment information for the utility task performance. To mitigate the challenge, we investigate an alternative approach by sharing Matrix Profile (MP), which is a non-linear transformation of original data and a versatile data structure that supports many data mining tasks. We found that while MP can prevent concrete shape leakage, the canonical correlation in MP index can still reveal the location of sensitive long pattern. Based on this observation, we design two attacks named Location Attack and Entropy Attack to extract the pattern location from MP. To further protect MP from these two attacks, we propose a Privacy-Aware Matrix Profile (PMP) via perturbing the local correlation and breaking the canonical correlation in MP index vector. We evaluate our proposed PMP against baseline noise-adding methods through quantitative analysis and real-world case studies to show the effectiveness of the proposed method

Privacy-preserving distributed data mining

This thesis is concerned with privacy-preserving distributed data mining algorithms. The main challenges in this setting are inference attacks and the formation of collusion groups. The inference problem is the reconstruction of sensitive data by attackers from non-sensitive sources, such as intermediate results, exchanged messages, or public information. Moreover, in a distributed scenario, malicious insiders can organize collusion groups to deploy more effective inference attacks. This thesis shows that existing privacy measures do not adequately protect privacy against inference and collusion. Therefore, in this thesis, new measures based on information theory are developed to overcome the identiffied limitations. Furthermore, a new distributed data clustering algorithm is presented. The clustering approach is based on a kernel density estimates approximation that generates a controlled amount of ambiguity in the density estimates and provides privacy to original data. Besides, this thesis also introduces the first privacy-preserving algorithms for frequent pattern discovery in a distributed time series. Time series are transformed into a set of n-dimensional data points and finding frequent patterns reduced to finding local maxima in the n-dimensional density space. The proposed algorithms are linear in the size of the dataset with low communication costs, validated by experimental evaluation using different datasets.Diese Arbeit befasst sich mit vertraulichkeitsbewahrendem Data Mining in verteilten Umgebungen mit Schwerpunkt auf ausgewählten N-Agenten-Angriffsszenarien für das Inferenzproblem im Data-Clustering und der Zeitreihenanalyse. Dabei handelt es sich um Angriffe von einzelnen oder Teilgruppen von Agenten innerhalb einer verteilten Data Mining-Gruppe oder von einem einzelnen Agenten außerhalb dieser Gruppe. Zunächst werden in dieser Arbeit zwei neue Privacy-Maße vorgestellt, die im Gegensatz zu bislang existierenden, die im verteilten Data Mining allgemein geforderte Eigenschaften zur Vertraulichkeitsbewahrung erfüllen und bei denen sich der gemessene Grad der Vertraulichkeit auf die verwendete Datenanalysemethode und die Anzahl von Angreifern bezieht. Für den Zweck eines vertraulichkeitsbewahrenden, verteilten Data-Clustering wird ein neues Kernel-Dichteabschätzungsbasiertes Verfahren namens KDECS vorgestellt. KDECS verwendet eine Approximation der originalen, lokalen Kernel-Dichteschätzung, so dass die ursprünglichen Daten anderer Agenten in der Data Mining-Gruppe mit einer höheren Wahrscheinlichkeit als einem hierfür vorgegebenen Wert nicht mehr zu rekonstruieren sind. Das Verfahren ist nachweislich sicherer als Data-Clustering mit generativen Mixture Modellen und SMC-basiert sicherem k-means Data-Clustering. Zusätzlich stellen wir neue Verfahren, namens DPD-TS, DPD-HE und DPDFS, für eine vertraulichkeitsbewahrende, verteilte Mustererkennung in Zeitreihen vor, deren Komplexität und Sicherheitsgrad wir mit den zuvor erwähnten neuen Privacy-Maßen analysieren. Dabei hängt ein von einzelnen Agenten einer Data Mining-Gruppe jeweils vorgegebener, minimaler Sicherheitsgrad von DPD-TS und DPD-FS nur von der Dimensionsreduktion der Zeitreihenwerte und ihrer Diskretisierung ab und kann leicht überprüft werden. Einen noch besseren Schutz von sensiblen Daten bietet das Verfahren DPD HE mit Hilfe von homomorpher Verschlüsselung. Neben der theoretischen Analyse wurden die experimentellen Leistungsbewertungen der entwickelten Verfahren mit verschiedenen, öffentlich verfügbaren Datensätzen durchgeführt

Litigation Isolationism

Over the past two decades, U.S. courts have pursued a studied avoidance of transnational litigation. The resulting litigation isolationism appears to be driven by courts’ desire to promote separation of powers, international comity, and the interests of defendants. This Article demonstrates, however, that this new kind of “avoidance” in fact frequently undermines not only these values but also other significant U.S. interests by continuing to interfere with foreign relations and driving plaintiffs to sue in foreign courts. This Article offers four contributions: First, it focuses the conversation about transnational litigation on those doctrines designed to avoid it—that is, doctrines that permit or require courts to dismiss a case based on its “foreignness.” Doing so helps to identify the particular concerns justifying this kind of avoidance and to evaluate them on their own terms. Second, the Article presents evidence of emerging foreign trends that increasingly (and surprisingly) permit traditionally American, plaintiff-friendly procedures, including higher damages awards, aggregate litigation, and third-party litigation financing. Third, the Article demonstrates that, particularly in light of these foreign trends, avoidance has failed to achieve its stated goals, and in many instances has undermined them. Finally, the Article suggests ways to refine avoidance doctrines to address these unintended consequences. Its more basic and urgent task, however, is to identify the growing phenomenon of litigation isolationism, highlight its perversities, and caution against its further expansion

The Unsung Virtues of Global Forum Shopping

Forum shopping gets a bad name. This is even more true in the context of transnational litigation. The term is associated with unprincipled gamesmanship and undeserved victories. Courts therefore often seek to thwart the practice. But in recent years, exaggerated perceptions of the “evils” of forum shopping among courts in different countries have led U.S. courts to impose high barriers to global forum shopping. These extreme measures prevent global forum shopping from serving three unappreciated functions: protecting access to justice, promoting private regulatory enforcement, and fostering legal reform. This Article challenges common perceptions about global forum shopping that have supported recent doctrinal developments. It traces the history of concerns about global forum shopping and distinguishes between domestic and global forum shopping to discern the core objections to the practice. It then identifies these unappreciated virtues of global forum shopping and suggests balanced ways for courts to protect them

Non-fungible tokens (NFTS) and their security challenges

The Non-Fungible Token (NFT) market has been exploding in the past years. The notion of NFT originated with Ethereum's token standard, which aimed to differentiate each token using distinguishing signals. Tokens of this type can be associated with virtual or digital properties to serve as unique identifiers. Using NFTs Non-Fungible Token (NFT) is a new technology gaining traction in the Blockchain industry. In this article, we examine state-of the art NFT systems that have the potential to reshape the market for digital virtual assets. We will assess the security of existing NFT systems and expand on the opportunities and prospective uses for the NFT idea. Finally, we discuss existing research challenges that must be overcome before mass-market penetration may occur. We hope that this paper provides an up-to-date analysis and summary of existing and proposed solutions and projects, making it easier for newcomers to stay current.Fonksuz Belirteç (NFT) pazarı son yıllarda patlama yapıyor. NFT'nin nosyonu Ethereum'un belirteç standardıyla ortaya çıkmıştır ve bu durum, her belirteci ayırt edici sinyaller kullanarak ayırt etmeyi amaçlamaktadır. Bu tipteki belirteçler, benzersiz tanımlayıcılar olarak hizmet vermek için sanal veya dijital özelliklerle ilişkilendirilebilir. NFTS Non-Fungible Token (NFT) kullanmak, Blockchain endüstrisinde yeni bir teknoloji kazanıyor. Bu makalede, dijital sanal varlıklar için pazarı yeniden şekillendirme potansiyeline sahip son teknoloji ürünü NFT sistemlerini inceliyoruz. Mevcut NFT sistemlerinin güvenliğini değerlendirecek ve NFT fikri için fırsatları ve olası kullanımları genişleteceğiz. Son olarak, kitle pazara giriş gerçekleşmeden önce aşılması gereken mevcut araştırma zorluklarını ele alıyoruz. Bu incelemede, mevcut ve önerilen çözüm ve projelerin güncel bir analizi ve özeti sağlanarak, yeni gelenlerin güncel kalmasını kolaylaştırılmasını umuyoruz.No sponso

Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives

Uncovering, Disclosing, and Discovering How the Public Dimensions of Court-Based Processes Are at Risk

In this essay—considering privacy and secrecy in courts—I first offer a brief history of the public performance, through adjudication, of the power of rulers, who relied on open rituals of judgment and punishment to make and maintain law and order. Second, I turn to consider why, during the twentieth century, the federal courts became an unusually good source of information about legal, political, and social conflict. Third, I map how, despite new information technologies, knowledge about conflicts and their resolution is being limited by the devolution of court authority to agencies, by the outsourcing of decisions to private providers, and by the internalization in courts of rules that promote private management and settlement of conflicts in lieu of adjudication. Fourth, I argue that deployment of new procedures of dispute resolution requires new answers to questions about what processes should be presumptively public and that, given their political implications, these answers should not be left to judges, as rulemakers or doctrine-producers alone. Fifth, I ex- plain why new regulations are needed to protect the public dimensions of courts and to create public dimensions for their alternatives. Public processes generate not only knowledge about the uses of power but also a commitment to fair treatment by government, to accountability in government, and to norm development, all of which should not be controlled exclusively by the parties to a dispute nor by those empowered to resolve it

Near Real-Time Sentiment and Topic Analysis of Sport Events

Sport events’ media consumption patterns have started transitioning to a multi-screen paradigm, where, through multitasking, viewers are able to search for additional information about the event they are watching live, as well as contribute with their perspective of the event to other viewers. The audiovisual and multimedia industries, however, are failing to capitalize on this by not providing the sports’ teams and those in charge of the audiovisual production with insights on the final consumers perspective of sport events. As a result of this opportunity, this document focuses on presenting the development of a near real-time sentiment analysis tool and a near real-time topic analysis tool for the analysis of sports events’ related social media content that was published during the transmission of the respective events, thus enabling, in near real-time, the understanding of the sentiment of the viewers and the topics being discussed through each event.Os padrões de consumo de media, têm vindo a mudar para um paradigma de ecrãs múltiplos, onde, através de multitasking, os telespetadores podem pesquisar informações adicionais sobre o evento que estão a assistir, bem como partilhar a sua perspetiva do evento. As indústrias do setor audiovisual e multimédia, no entanto, não estão a aproveitar esta oportunidade, falhando em fornecer às equipas desportivas e aos responsáveis pela produção audiovisual uma visão sobre a perspetiva dos consumidores finais dos eventos desportivos. Como resultado desta oportunidade, este documento foca-se em apresentar o desenvolvimento de uma ferramenta de análise de sentimento e uma ferramenta de análise de tópicos para a análise, em perto de tempo real, de conteúdo das redes sociais relacionado com eventos esportivos e publicado durante a transmissão dos respetivos eventos, permitindo assim, em perto de tempo real, perceber o sentimento dos espectadores e os tópicos mais falados durante cada evento

Quantum entanglement

All our former experience with application of quantum theory seems to say: {\it what is predicted by quantum formalism must occur in laboratory}. But the essence of quantum formalism - entanglement, recognized by Einstein, Podolsky, Rosen and Schr\"odinger - waited over 70 years to enter to laboratories as a new resource as real as energy. This holistic property of compound quantum systems, which involves nonclassical correlations between subsystems, is a potential for many quantum processes, including ``canonical'' ones: quantum cryptography, quantum teleportation and dense coding. However, it appeared that this new resource is very complex and difficult to detect. Being usually fragile to environment, it is robust against conceptual and mathematical tools, the task of which is to decipher its rich structure. This article reviews basic aspects of entanglement including its characterization, detection, distillation and quantifying. In particular, the authors discuss various manifestations of entanglement via Bell inequalities, entropic inequalities, entanglement witnesses, quantum cryptography and point out some interrelations. They also discuss a basic role of entanglement in quantum communication within distant labs paradigm and stress some peculiarities such as irreversibility of entanglement manipulations including its extremal form - bound entanglement phenomenon. A basic role of entanglement witnesses in detection of entanglement is emphasized.Comment: 110 pages, 3 figures, ReVTex4, Improved (slightly extended) presentation, updated references, minor changes, submitted to Rev. Mod. Phys

Regulating Information Flows: States, Private Actors and E-Commerce

Growing interdependence between jurisdictions means that states are increasingly using private actors as proxies, in order to achieve desired regulatory outcomes. International relations theory has had difficulty in understanding the exact circumstances under which they might wish to do this. Drawing on existing literatures in both international relations and legal scholarship, this article proposes a framework for understanding the circumstances under which states will or will not use private actors as proxy regulators. This framework highlights the relationship between state preferences and the presence or absence of points of control, a special kind of private actor. The article then conducts an initial plausibility probe of the framework, assessing how well it explains outcomes in the regulation of gambling, privacy, and the taxation of e-commerce