Reviewerly/exHarmony huggingface dataset:
Doctors Without Borders? The Law Applicable to Cross-Border eHealth Services and AI-Based Medicine

By their very nature eHealth applications – including telemedicine, AI-based medicine and ‘smart’ medical devices – are ubiquitous: These tools may be used by the physician next door as well as in the most remote locations abroad. Moreover, highly sensitive medical data may flow around the world within a split second. Against this backdrop, eHealth and telemedicine services can be provided from – and the necessary data can be transferred to – virtually every corner of the world. By contrast, the scope of application of regulation relating to AI-driven medicine, as well as eHealth and telemedicine, is usually confined to the legislating state. Moreover, the number and complexity of rules and regulations in this field vary considerably from state to state. Does this mean that international MedTech businesses may simply set up camp in the jurisdiction most favourable to their business models? For practitioners in telemedicine, the MedTech industry providing AI applications or digital medical devices such as eHealth apps, as well as for patients, it is essential to know which laws govern activities undertaken in cross-border scenarios. This concerns licensing requirements and the level of data protection as well as contract and tort law applicable to eHealth, telemedicine and telesurgery services.
Blockchain and Supply Chain Management

The use of blockchain technology as an end-to-end quality assurance monitoring tool is bound to become increasingly popular in the years to come, as evidenced by its successful adoption by such major entities as Walmart, Maersk, and DeBeers Jewelers. Blockchain addresses a significant constraint in supply chain management; namely the fact that often organizational information systems in a supply chain may not communicate fully, or at all. This constraint has traditionally limited the monitoring capacities of entities along a supply chain in some significant ways. This chapter discusses the various types and components of supply chains and identifies their major fraud risks, such as counterfeit goods, fraudulent billing, false claims, and misappropriation of assets. The chapter also provides an overview of some common food fraud schemes, such as product substitution, mislabeling, and adulteration. Most importantly, blockchain implementation benefits within various supply chains are outlined, discussed, and further demonstrated by looking at several case studies. The benefits of using blockchain technology for supply chain monitoring purposes include a higher level of quality assurance through more granular tracking and identity management, more effective and secure communication among various supply chain stakeholders, lower supply-chain related expenses – especially lower external failure costs – and more efficient administration of the entire supply chain.
Foreign Policy

Abstract The foreign policy of South Korea since the Sixth Republic was established in 1987 has remained remarkably stable. In terms of structures, goals, and tools, there has been little change across administrations. The structures underpinning South Korea’s foreign policymaking process reflect the country’s strong presidentialism. Thus, the president and its office dominate foreign policy decision-making. Throughout this period, the key goals have been strengthening national security, autonomy, Korean reunification, promoting economic openness at the global level, and influencing international affairs. When it comes to foreign policy tools, South Korea prioritizes its alliance with the United States, strong military capabilities, multilateralism and cooperation, soft power and public diplomacy, and trade multilateralism and bilateralism. Ultimately, South Korea is a middle power. Its foreign policy objectives and, especially, tools reflect this.
Cyber Insurance from the stakeholder's perspective

Business disruption from cyber-attacks is a recognized and growing concern, yet the uptake of cyber insurance has been substantially lower than expected. This study aimed to identify what factors may be influencing perceptions and uptake of cyber insurance. In-depth interviews were conducted with two stakeholder groups: those responsible for making cybersecurity decisions within businesses, and those involved in marketing cybersecurity products and/or services including cyber insurance. Thematic analysis generated five themes from the data: High complexity of company-level decision making, Security investment trade-off, Lack of risk data and immaturity of cyber insurance, Mistrust of insurers, and Compliance legislation as a driver for cyber insurance adoption. The results highlight the importance of recognizing that internal organizational decision making involves a complex eco-system which can make the process of obtaining and renewing cyber insurance an effortful process. Legislation may facilitate insurance uptake, but several external factors represent key barriers. There is a need for clearer policy wording, improved processes for cyber risk assessment, improved trust in insurers and lower policy premiums.
Vision: Design Fiction for Cybersecurity

Security and privacy issues are an ever-increasing problem for software systems. To address them, software developers must anticipate the problems that their developed systems may face, using a process we call ‘threat assessment’. Unfortunately, given the shortage of security experts, and the need to ‘think laterally’, threat assessment is very difficult for many development teams. One possibility is to use stories, known as ‘Design Fiction,’ to help developers visualize different contexts and future use for their software. But such stories are themselves difficult to write. A recent pilot project investigated using a broad-brush threat model and fiction samples derived from existing science fiction literature to help developers create threat assessments for Health Internet-of-Things devices. The preliminary results are encouraging, and open the possibility of developing a method to support developers in threat assessment in any domain.
Assessing Real-World Applicability of Redesigned Developer Documentation for Certificate Validation Errors

We face certificate validation errors commonly, yet the related tools and documentation had been shown to have very poor usability. Previous research suggests that just improving the error messages and corresponding documentation can have significantly positive effects. Our work aims at increasing the usability of certificate validation by 1) redesigning the API error messages and the corresponding documentation, and 2) validating the real-world applicability of the redesign by investigating the opinions of 180 IT professionals. We focus on the perceived obstacles, desired ideal form and overall satisfaction. The redesigned documentation exhibits a reliable significant decrease in perceived incompleteness, with a small amount of perceived bloat and tangle. The redesigned documentation, now published on a dedicated website, is preferred by 89% of our study participants.
TIKD: A Trusted Integrated Knowledge Dataspace for Sensitive Data Sharing and Collaboration

Abstract This chapter presents the Trusted Integrated Knowledge Dataspace (TIKD)—a trusted data sharing approach, based on Linked Data technologies, that supports compliance with the General Data Privacy Regulation (GDPR) for personal data handling as part of data security infrastructure for sensitive application environments such as healthcare. State-of-the-art shared dataspaces typically do not consider sensitive data and privacy-aware log records as part of their solutions, defining only how to access data. TIKD complements existing dataspace security approaches through trusted data sharing that includes personal data handling, data privileges, pseudonymization of user activity logging, and privacy-aware data interlinking services. TIKD was implemented on the Access Risk Knowledge (ARK) Platform, a socio-technical risk governance system, and deployed as part of the ARK-Virus Project which aims to govern the risk management of personal protection equipment (PPE) across a group of collaborating healthcare institutions. The ARK Platform was evaluated, both before and after implementing the TIKD, using both the ISO 27001 Gap Analysis Tool (GAT), which determines information security standard compliance, and the ISO 27701 standard for privacy information. The results of the security and privacy evaluations indicated that compliance with ISO 27001 increased from 50% to 85% and compliance with ISO 27701 increased from 64% to 90%. This shows that implementing TIKD provides a trusted data security dataspace with significantly improved compliance with ISO 27001 and ISO 27701 standards to share data in a collaborative environment.
Technological Perspective of Data Governance in Data Space Ecosystems

Abstract Data has been identified as a valuable input to boost enterprises. Nowadays, with the vast quantity of data available, a favorable scenario is established to exploit it, but crucial challenges must be addressed, highlighting its sharing and governance. In this context, the data space ecosystem is the cornerstone which enables companies to share and use valuable data assets. However, appropriate Data Governance techniques must be established to benefit from such opportunity considering two levels: internal to the organization and at the level of sharing between organizations. At a technological level, to reach this scenario, companies need to design and provision adequate data platforms to deal with Data Governance in order to cover the data life-cycle. In this chapter, we will address questions such as: How to share data and extract value while maintaining sovereignty over data, confidentiality, and fulfilling the applicable policies and regulations? How does the Big Data paradigm and its analytical approach affect correct Data Governance? What are the key characteristics of the data platforms to be covered to ensure the correct management of data without losing value? This chapter explores these challenges providing an overview of state-of-the-art techniques.
Increasing Trust for Data Spaces with Federated Learning

Abstract Despite the need for data in a time of general digitization of organizations, many challenges are still hampering its shared use. Technical, organizational, legal, and commercial issues remain to leverage data satisfactorily, specially when the data is distributed among different locations and confidentiality must be preserved. Data platforms can offer “ad hoc” solutions to tackle specific matters within a data space. MUSKETEER develops an Industrial Data Platform (IDP) including algorithms for federated and privacy-preserving machine learning techniques on a distributed setup, detection and mitigation of adversarial attacks, and a rewarding model capable of monetizing datasets according to the real data value. The platform can offer an adequate response for organizations in demand of high security standards such as industrial companies with sensitive data or hospitals with personal data. From the architectural point of view, trust is enforced in such a way that data has never to leave out its provider’s premises, thanks to federated learning. This approach can help to better comply with the European regulation as confirmed from a legal perspective. Besides, MUSKETEER explores several rewarding models based on the availability of objective and quantitative data value estimations, which further increases the trust of the participants in the data space as a whole.
Short Lattice Signature Scheme with Tighter Reduction under Ring-SIS Assumption

We propose a short signature scheme under the ring-SIS assumption in the standard model. Specifically, by revisiting an existing construction [Ducas and Micciancio, CRYPTO 2014], we demonstrate lattice-based signatures with improved reduction loss. As far as we know, there are no ways to use multiple tags in the signature simulation of security proof in the lattice tag-based signatures. We address the tag-collision possibility in the lattice setting, which improves reduction loss. Our scheme generates tags from messages by constructing a scheme under a mild security condition that is existentially unforgeable against random message attack with auxiliary information. Thus our scheme can reduce the signature size since it does not need to send tags with the signatures. Our scheme has short signature sizes of O(1) and achieves tighter reduction loss than that of Ducas et al.'s scheme. Our proposed scheme has two variants. Our scheme with one property has tighter reduction and the same verification key size of O(log n) as that of Ducas et al.'s scheme, where n is the security parameter. Our scheme with the other property achieves much tighter reduction loss of O(Q/n) and verification key size of O(n), where Q is the number of signing queries.
Profiling Deep Learning Side-Channel Attacks Using Multi-Label against AES Circuits with RSM Countermeasure

Deep learning side-channel attacks (DL-SCAs) have been actively studied in recent years. In the DL-SCAs, deep neural networks (DNNs) are trained to predict the internal states of the cryptographic operation from the side-channel information such as power traces. It is important to select suitable DNN output labels expressing an internal states for successful DL-SCAs. We focus on the multi-label method proposed by Zhang et al. for the hardware-implemented advanced encryption standard (AES). They used the power traces supplied from the AES-HD public dataset, and reported to reveal a single key byte on conditions in which the target key was the same as the key used for DNN training (profiling key). In this paper, we discuss an improvement for revealing all the 16 key bytes in practical conditions in which the target key is different from the profiling key. We prepare hardware-implemented AES without SCA countermeasures on ASIC for the experimental environment. First, our experimental results show that the DNN using multi-label does not learn side-channel leakage sufficiently from the power traces acquired with only one key. Second, we report that DNN using multi-label learns the most of side-channel leakage by using three kinds of profiling keys, and all the 16 target key bytes are successfully revealed even if the target key is different from the profiling keys. Finally, we applied the proposed method, DL-SCA using multi-label and three profiling keys against hardware-implemented AES with rotating S-boxes masking (RSM) countermeasures. The experimental result shows that all the 16 key bytes are successfully revealed by using only 2,000 attack traces. We also studied the reasons for the high performance of the proposed method against RSM countermeasures and found that the information from the weak bits is effectively exploited.
White-box adversarial attacks

In this chapter, we introduce the procedure of adversarial attacks and describe how to conduct an adversarial attack in the white-box setting, where the attack is assumed to have full knowledge about the target model. In this setting, we show that adversarial attacks can be formulated as optimization problems and discuss a widely used gradient-based optimizer for solving the problem. We also introduce several different attack goals, including untargeted, targeted, and universal attacks.
Adversarial attacks beyond image classification

This chapter provides some examples and the rule of thumb for conducting adversarial attacks on tasks other than classification and data modalities other than images.
Background and motivation

This chapter introduces mathematical notations and machine learning basics, and provides examples to motivate the study of adversarial robustness for machine learning. Finally, we provide some links to open-source Python-based libraries for adversarial robustness.
Model watermarking and fingerprinting

In this chapter, we introduce how the techniques used in adversarial attacks, including backdoors and adversarial examples, can be leveraged for model watermarking and fingerprinting, which are crucial to model ownership verification and model governance.
Randomization-based defense

Besides adversarial training, another successful defense approach is to inject randomness into the model. In this chapter, we introduce this family of randomization-based defense methods.
Overview of adversarial defense

Adversarial defense methods have been developed to improve the robustness of neural networks. In this chapter, we discuss the challenges of developing and evaluating a defense method and describe the difference between empirical defense and certified defense. We also briefly introduce several representative defense techniques by categorizing them into several families. More details of some commonly used attack strategies will be discussed in the next few chapters.
Training-time adversarial attacks

This chapter introduces two types of training-phase adversarial attacks, poisoning attack and backdoor attack. Then we provide a case study on the robustness of federated learning algorithms against distributed backdoor attacks.
Data augmentation for unsupervised machine learning

Current studies on adversarial examples focus on supervised learning tasks, relying on the ground-truth data label, a targeted objective, or supervision from a trained classifier. In this chapter, we introduce a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation. When using the resulting unsupervised adversarial examples as a simple plug-in data augmentation tool for model retraining, significant improvements are consistently observed across different unsupervised tasks and datasets, including data reconstruction, representation learning, and contrastive learning.
Complete neural network verification

Although computing the exact robustness radius via complete verification is difficult, recent progress has enabled complete verification for realistic neural networks on medium-scale datasets. In this chapter, we discuss the recent progress in complete verification.
Overview of neural network verification

Adversarial attacks, despite being an effective tool to find “bugs” in machine learning models, do not provide a reliable and provable measurement of robustness. For applications such as self-driving cars and flight control systems, 100% safety has to be guaranteed. Neural network verification aims to provide provable safety and robustness guarantees for these applications. We will define the concept of neural network verification and discuss use cases in this chapter.
Black-box adversarial attacks

This chapter starts by introducing the taxonomy of evasion attacks based on the knowledge of an attacker. Then this chapter provides a deep dive into three black-box attack settings: soft-label, hard-label, and transfer attacks.
Adversarial robustness of beyond neural network models

In this chapter, we discuss the robustness verification problem for other nonneural network models, including tree-based models and nearest-neighbor classifiers. Due to the discrete nature of these models, their robustness evaluation methods are very different from neural networks. We discuss how to evaluate and enhance the robustness of these models.
Adversary detection

In this chapter, we discuss several methods for detecting adversarial inputs or compromised models against training-phase and deployment-phase attacks.
Adversarial training

Adversarial training is one of the most popular methods to improve the adversarial robustness of neural network models. As the goal of adversarial defense is training a model to minimize the robust error against worst-case perturbations within a certain distance, this training objective can be naturally written as a bilevel optimization, where an attacker aims to obtain the worst-case perturbation in the inner loop, and the defender aims to train the model that minimizes the error with the perturbed example. In this chapter, we introduce this bilevel optimization formulation of adversarial training and discuss various improvements on both speed and accuracy over the canonical adversarial training.
Verification against semantic perturbations

This chapter focuses on how to extend the robustness verification tools for ℓp norms to a set of semantic perturbations for images, such as semantic changes in hue, saturation, lightness, brightness, contrast, and rotation.
Incomplete neural network verification

In this chapter, we introduce incomplete neural network verification methods. Instead of computing the exact robustness score (shortest distance from a sample to the decision boundary), they aim to find a lower bound of it using efficient bound propagation algorithms. We introduce the convex relaxation framework, which serves as a foundation of almost all the incomplete verification methods, and then introduce some representative methods in more detail.
Instruction Selection

The compiler's front end and optimizer both operate on the code in its ir form. To create executable code, the compiler must rewrite the ir into the processor's instruction set. This process is called instruction selection. Because a typical processor provides multiple ways to express most computations, the selector must choose the best sequence from among multiple implementations. This chapter introduces two distinct approaches to instruction selection. The first builds on peephole optimization, a classic late-stage code-improvement technique. The second uses tree-pattern matching algorithms to find a set of operations that implement the ir program. Both approaches are supported by reliable tools. Both have found widespread use in real compilers.
Introduction to Optimization

To improve the quality of code that a compiler generates, its optimizer analyzes that code and rewrites it into a more efficient form. This chapter introduces the problems and techniques of code optimization and presents key concepts through a series of example optimizations. Chapter 9 expands on this material with a deeper exploration of program analysis. Chapter 10 provides a broader coverage of optimizing transformations. This chapter examines how optimization can improve code. It discusses the safety and profitability of optimizations, along with the kinds of opportunities for improvement that a compiler writer should seek. It lays out the various scopes at which optimization is performed and presents two examples at each scope.
Parsers

The parser's task is to determine if the input program, represented by the stream of classified words produced by the scanner, is a valid sentence in the programming language. To do so, the parser attempts to build a derivation for the input program, in a grammar that describes the programming language. This chapter introduces context-free grammars to specify programming language syntax. It develops both top-down and bottom-up parsing techniques. It describes ways to automate the construction of both of these types of parsers. Finally, it explores a number of practical issues that arise in parser construction.
Implementing Procedures

Procedures play a critical role in the development of software systems. They provide abstractions for control flow and naming. They provide basic information hiding. They are the building block on which systems provide interfaces. They are one of the principal forms of abstraction in Algol-like languages; object-oriented languages rely on procedures to implement their methods or code members. This chapter takes an in-depth look at the implementation of procedures and procedure calls, from the perspective of a compiler writer. It highlights the implementation similarities and differences between Algol-like languages and object-oriented languages. The Advanced Topics section presents a brief introduction to the algorithms used to manage the runtime heap.