Catch, Clean, and Release: A Survey of Obstacles and Opportunities for Network Trace Sanitization

Network researchers benefit tremendously from access to traces of production networks, and several repositories of such network traces exist. By their very nature, these traces capture sensitive business and personal activity. Furthermore, network traces contain significant operational information about the target network, such as its structure, identity of the network provider, or addresses of important servers. To protect private or proprietary information, researchers must “sanitize” a trace before sharing it. \par In this chapter, we survey the growing body of research that addresses the risks, methods, and evaluation of network trace sanitization. Research on the risks of network trace sanitization attempts to extract information from published network traces, while research on sanitization methods investigates approaches that may protect against such attacks. Although researchers have recently proposed both quantitative and qualitative methods to evaluate the effectiveness of sanitization methods, such work has several shortcomings, some of which we highlight in a discussion of open problems. Sanitizing a network trace, however challenging, remains an important method for advancing network–based research

Large-scale Wireless Local-area Network Measurement and Privacy Analysis

The edge of the Internet is increasingly becoming wireless. Understanding the wireless edge is therefore important for understanding the performance and security aspects of the Internet experience. This need is especially necessary for enterprise-wide wireless local-area networks (WLANs) as organizations increasingly depend on WLANs for mission- critical tasks. To study a live production WLAN, especially a large-scale network, is a difficult undertaking. Two fundamental difficulties involved are (1) building a scalable network measurement infrastructure to collect traces from a large-scale production WLAN, and (2) preserving user privacy while sharing these collected traces to the network research community. In this dissertation, we present our experience in designing and implementing one of the largest distributed WLAN measurement systems in the United States, the Dartmouth Internet Security Testbed (DIST), with a particular focus on our solutions to the challenges of efficiency, scalability, and security. We also present an extensive evaluation of the DIST system. To understand the severity of some potential trace-sharing risks for an enterprise-wide large-scale wireless network, we conduct privacy analysis on one kind of wireless network traces, a user-association log, collected from a large-scale WLAN. We introduce a machine-learning based approach that can extract and quantify sensitive information from a user-association log, even though it is sanitized. Finally, we present a case study that evaluates the tradeoff between utility and privacy on WLAN trace sanitization

The Data Trust Solution to Data Sharing Problems

A small number of large companies hold most of the world’s data. Once in the hands of these companies, data subjects have little control over the use and sharing of their data. Additionally, this data is not generally available to small and medium enterprises or organizations who seek to use it for social good. A number of solutions have been proposed to limit Big Tech “power,” including antitrust actions and stricter privacy laws, but these measures are not likely to address both the oversharing and under-sharing of personal data. Although the data trust concept is being actively explored in the United Kingdom, European Union, and Canada, this is the first Article to take an in-depth look at the viability of data trusts from a US perspective. A data trust is a governance device that places an independent fiduciary intermediary between Big Tech and human data subjects. This Article explores how data trusts might be configured as bundles of contracts in the information supply chain. In addition to their benefits for the social good, data trusts might contribute to relieve some of the tension between EU and US privacy practices

Novel reversible text data de-identification techniques based on native data structures

Technological development in today's digital world has resulted in the collection and storage of large amounts of personal data. These data enable both direct services and non-direct activities, known as secondary use. The secondary use of data can improve decision-making, service experiences, and healthcare systems. However, the widespread reuse of personal data raises significant privacy and policy issues, especially for health- related information; these data may contain sensitive data, leading to privacy breaches if compromised. Legal systems establish laws to protect the privacy of personal data disclosed for secondary use. A well-known example is the General Data Protection Regulation (GDPR), which outlines a specific set of rules for sharing and storing personal data to protect individual privacy. The GDPR explicitly points to data de-identification, especially pseudonymization, as one measure that can help meet the requirements for the processing of personal data. The literature on privacy preservation approaches has largely been developed in the field of data anonymization, where personal data are irreversibly removed or obfuscated and there is no means by which to recover an individual's identity if needed. By contrast, pseudonymization is a promising technique to protect privacy while enabling the recovery of de-identified data. Significantly, many existing approaches for pseudonymization were developed long before the GDPR requirements were established, and so they may fail to satisfy its provisions. Therefore, it is worthwhile to offer technical solutions to preserve privacy while supporting the legitimate use of data. This thesis proposes a novel de-identification system for unstructured textual data, known as ARTPHIL, that generates de-identified data in compliance with the GDPR requirement for strong pseudonymization. The system was evaluated using 2014 i2b2 testing data. The proposed system achieved a recall of 96.93% in terms of detecting and encrypting personal health information, as specified under guidelines provided by the Health Insurance Portability and Accountability Act (HIPAA). The system used a novel and lightweight cryptography algorithm E-ART to encrypt personal data cost-effectively and without compromising security. The main novelty of the E-ART algorithm is the use of the reflection property of a balanced binary tree data structure as substitution method instead of complex and multiple iterations. The performance and security of the proposed algorithm were compared to two symmetric encryption algorithms: The Advanced Encryption Standard and Data Encryption Standard. The security analysis showed comparable results, but the performance analysis indicated that E‐ART had the shortest ciphertext and running time with comparable memory usage, which indicates the feasibility of using ARTPHIL for delay-sensitive or data-intensive application

Cyber Threat Intelligence Exchange

The processing and exchange of Cyber Threat Intelligence (CTI) has become an increas- ingly important topic in recent years. This trend can be attributed to various factors. On the one hand, the exchange of information offers great potential to strengthen the knowledge base of companies and thus improve their protection against cyber threats. On the other hand, legislators in various countries have recognized this potential and translated it into legal reporting requirements. However, CTI is still a very young research area with only a small body of literature. Hence, there are hardly any guidelines, uniform standards, or specifications that define or support such an exchange. This dissertation addresses the problem by reviewing the methodological foundations for the exchange of threat intelligence in three focal areas. First, the underlying data formats and data structures are analyzed, and the basic methods and models are developed. In the further course of the work, possibilities for integrating humans into the analysis process of security incidents and into the generation of CTI are investigated. The final part of the work examines possible obstacles in the exchange of CTI. Both the legal environment and mechanisms to create incentives for an exchange are studied. This work thus creates a solid basis and a structured framework for the cooperative use of CTI

European Privacy by Design [védés előtt]

Three competing forces are shaping the concept of European Privacy by Design (PbD): laws and regulations, business goals and architecture designs. These forces carry their own influence in terms of ethics, economics, and technology. In this research we undertook the journey to understand the concept of European PbD. We examined its nature, application, and enforcement. We concluded that the European PbD is under-researched in two aspects: at organizational level (compared to the individual level); and mainly in the way it is enforced by authorities. We had high hopes especially with regards to the latter, and eager to bring significant scientific contribution on this field. We were interested to learn if data protection authorities are having such impacts looking at European PbD, that can pioneer new approaches to privacy preservation. This is why we elaborated on possible ways to measure their activity, in a manner that both legal and non-legal experts can understand our work. We promised a response to the research question can the enforcement of European PbD be measured and if yes, what are possible ways to do so? We conducted data analytics on quantitative and qualitative data to answer this question the best way possible. Our response is a moderate yes, the enforcement of PbD can be measured. Although, at this point, we need to settle with only good-enough ways of measure and not dwell into choosing the most optimal or best ways. One reason for this is that enforcement of PbD cases are highly customized and specific to their own circumstances. We have shown this while creating models to predict the amount of administrative fines for infringement of GDPR. Clustering these cases was a daunting task. Second reason for not delivering what could be the best way of measure is lack of data availability in Europe. This problem has its roots in the philosophical stance that the European legislator is taking on the topic of data collection within the EU. Lawmakers in Europe certainly dislike programs that collect gigantic amounts of personal data from EU citizens. Third reason is a causal link between the inconsistent approach between the data protection authorities’ practices. This is due to the different levels of competencies, reporting structures, personnel numbers, and experience in the work of data protection authorities. Looking beyond the above limitations, there are certainly ways to measure the enforcement of European PbD. Our measurements helped us formulate the following statements: a. The European PbD operates in ‘data saver’ mode: we argue that analogous to the data saving mode on mobile phones, where most applications and services get background data only via Wi-Fi connection, in Europe data collection and data processing is kept to minimal. Therefore, we argue that European PbD is in essence about data minimization. Our conviction that this concept is more oriented towards data security have been partially refuted. b. The European PbD is platform independent: we elaborated in the thesis on various infrastructures and convergent technologies that found compatibility with the PbD principles. We consider that the indeed the concept is evolutionary and technology –neutral. c. The European PbD is a tool obligation: we argue that the authorities are looking at PbD as a tool utilization obligation. In a simple language, companies should first perform a privacy impact assessment in order to find out which tools are supporting their data processing activities and then implement these, as mandated PbD. d. The European PbD is highly territorial: we reached the conclusion that enforcement of PbD is highly dependent on geographical indicators (i.e. countries and counties). The different level of privacy protection cultures are still present in Europe. On a particular level, what is commonly true across all countries is that European PbD mandates strong EU data sovereignty

Winter is Here: The Impossibility of Schrems II for U.S.-Based Direct-to-Consumer Companies

In this paper, Vanessa Zimmer exposes the precarious position of Direct-to-Consumer (DTC) companies that are physically located in the United States but still subject to the European General Data Protection Regulation (GDPR) under Article 3(2) because they offer goods or services to European consumers online. Standard Contractual Clauses (SCCs) and supplementary measures have dominated privacy conversions in the year since the European Court of Justice invalidated the EU-U.S. Privacy Shield framework with its Schrems II decision. However, Zimmer argues that the greater issue for U.S.-based DTC companies is the lack of clarity over what constitutes an international, or restricted, transfer under the GDPR in the first place. Is an international transfer any physical transfer of personal data from within the European Economic Area to outside its borders (the so-called “geographic” definition of international transfer) regardless of whether the foreign recipient is already directly subject to the GDPR? Or, is an international transfer only considered such if the recipient is located outside of the European Economic Area and not already directly subject to the GDPR (the so-called “jurisdictional” definition of international transfer)? Zimmer explains the rationale for each position and ultimately argues in favor of a jurisdictional definition of international transfers. The European Data Protection Board of the European Commission (the EDPB) and individual Member State supervisory authorities have repeatedly failed to define international transfers since the passage of the GDPR. This repeated failure to clarify the interplay between the territorial scope of the GDPR under Article 3(2) and the transfer restrictions of the GDPR under Chapter V has left U.S.-based DTC businesses uncertain of whether they are making international transfers under the GDPR and whether they must subsequently implement safeguards, such as SCCs, to protect those transfers. Zimmer explains how the Schrems II decision exposed the EDPB’s failure and exacerbated the already uncertain status of European personal data processing by U.S.-based DTC companies. The EDPB has further complicated the status of international transfers in its post-Schrems II guidance and its issuance of new SCCs for international transfers. Zimmer contends that it is vital for the sake of transatlantic trade and the continued integrity of the EDPB that the EDPB clearly defines international transfers and explains the applicability of transfer mechanisms to U.S.-based DTC companies

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Challenges in Cybersecurity and Privacy - the European Research Landscape

Cybersecurity and Privacy issues are becoming an important barrier for a trusted and dependable global digital society development. Cyber-criminals are continuously shifting their cyber-attacks specially against cyber-physical systems and IoT, since they present additional vulnerabilities due to their constrained capabilities, their unattended nature and the usage of potential untrustworthiness components. Likewise, identity-theft, fraud, personal data leakages, and other related cyber-crimes are continuously evolving, causing important damages and privacy problems for European citizens in both virtual and physical scenarios. In this context, new holistic approaches, methodologies, techniques and tools are needed to cope with those issues, and mitigate cyberattacks, by employing novel cyber-situational awareness frameworks, risk analysis and modeling, threat intelligent systems, cyber-threat information sharing methods, advanced big-data analysis techniques as well as exploiting the benefits from latest technologies such as SDN/NFV and Cloud systems. In addition, novel privacy-preserving techniques, and crypto-privacy mechanisms, identity and eID management systems, trust services, and recommendations are needed to protect citizens’ privacy while keeping usability levels. The European Commission is addressing the challenge through different means, including the Horizon 2020 Research and Innovation program, thereby financing innovative projects that can cope with the increasing cyberthreat landscape. This book introduces several cybersecurity and privacy research challenges and how they are being addressed in the scope of 15 European research projects. Each chapter is dedicated to a different funded European Research project, which aims to cope with digital security and privacy aspects, risks, threats and cybersecurity issues from a different perspective. Each chapter includes the project’s overviews and objectives, the particular challenges they are covering, research achievements on security and privacy, as well as the techniques, outcomes, and evaluations accomplished in the scope of the EU project. The book is the result of a collaborative effort among relative ongoing European Research projects in the field of privacy and security as well as related cybersecurity fields, and it is intended to explain how these projects meet the main cybersecurity and privacy challenges faced in Europe. Namely, the EU projects analyzed in the book are: ANASTACIA, SAINT, YAKSHA, FORTIKA, CYBECO, SISSDEN, CIPSEC, CS-AWARE. RED-Alert, Truessec.eu. ARIES, LIGHTest, CREDENTIAL, FutureTrust, LEPS. Challenges in Cybersecurity and Privacy - the European Research Landscape is ideal for personnel in computer/communication industries as well as academic staff and master/research students in computer science and communications networks interested in learning about cyber-security and privacy aspects