DORA :: Browsing by Author "Wagner, Isabel"

Browsing by Author "Wagner, Isabel"

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Open Access
Behavior-Based Outlier Detection for Network Access Control Systems
(ACM, 2019-07) Muhammad, Musa Abubakar; Ayesh, Aladdin, 1972-; Wagner, Isabel
Network Access Control (NAC) systems manage the access of new devices into enterprise networks to prevent unauthorised devices from attacking network services. The main difficulty with this approach is that NAC cannot detect abnormal behaviour of devices connected to an enterprise network. These abnormal devices can be detected using outlier detection techniques. Existing outlier detection techniques focus on specific application domains such as fraud, event or system health monitoring. In this paper, we review attacks on Bring Your Own Device (BYOD) enterprise networks as well as existing clustering-based outlier detection algorithms along with their limitations. Importantly, existing techniques can detect outliers, but cannot detect where or which device is causing the abnormal behaviour. We develop a novel behaviour-based outlier detection technique which detects abnormal behaviour according to a device type profile. Based on data analysis with K-means clustering, we build device type profiles using Clustering-based Multivariate Gaussian Outlier Score (CMGOS) and filter out abnormal devices from the device type profile. The experimental results show the applicability of our approach as we can obtain a device type profile for five dell-netbooks, three iPads, two iPhone 3G, two iPhones 4G and Nokia Phones and detect outlying devices within the device type profile.
Open Access
CAESAR8: An agile enterprise architecture approach to managing information security risks.
(Elsevier, 2022-08-17) Loft, Paul; He, Ying; Yevseyeva, Iryna; Wagner, Isabel
In theory, implementing an Enterprise Architecture (EA) should enable organizations to increase the accuracy of information security risk assessments. In reality, however, organizations struggle to fully implement EA frameworks because the requirements for implementing an EA and the benefits of commercial frameworks are unclear, and the overhead of maintaining EA artifacts is unacceptable, especially for smaller organizations. In this paper, we describe a novel approach called CAESAR8 (Continuous Agile Enterprise Security Architecture Review in 8 domains) that supports dynamic and holistic reviews of information security risks in IT projects. CAESAR8’s nonlinear design supports continuous reassessment of information security risks, based on a checklist that assesses the maturity of security considerations in eight domains that often cause information security failures. CAESAR8 assessments can be completed by multiple stakeholders independently, thus ensuring consideration of their tacit knowledge while preventing groupthink. Our evaluation with experienced industry professionals showed that CAESAR8 successfully addresses real-world problems in information security risk management, with significant benefits particularly for smaller organizations.
Open Access
Challenges in assessing privacy impact: Tales from the front lines
(John Wiley & Sons, 2019-12-13) Ferra, Fenia; Wagner, Isabel; Boiten, Eerke Albert; Hadlington, Lee; Psychoula, Ismini; Snape, J. Richard
Data protection impact assessments (DPIAs) aim to identify, rank, and mitigate privacy risks. Even though DPIAs are legally mandated in some cases and privacy professionals perform DPIAs on a daily basis, facilitating the systematic measurement of privacy risks is an open problem. Research on privacy risk measurement often does not take into account the practical needs and requirements for DPIAs in real organizations. In this article, we fill this gap by reporting on focus groups we held with a diverse group of privacy professionals. Through thematic analysis, we identify three themes that emerged from the focus groups: (a) how privacy in the contemporary society affects privacy risk assessment; (b) current practices and procedures in privacy risk assessment; and (c) common issues and challenges. Based on these themes, we identify future research directions for privacy risk measurement. Our article can help to ground research on privacy risk measurement in practical challenges faced by privacy professionals.
Open Access
Changes in Conducting Data Protection Risk Assessment: Before and After GDPR implementation
(arxiv, 2023-04-24) Zarrabi, Jorshari Fatemeh; Wagner, Isabel; Boiten, Eerke Albert
Based on Article 35 of the EU (European Union) General Data Protection Regu- lation, a Data Protection Impact Assessment (DPIA) is necessary whenever there is a possibility of a high privacy and data protection risk to individuals caused by a new project under development. A similar process to DPIA had been previously known as Privacy Impact Assessment (PIA). We are investigating here to find out if GDPR and DPIA specifically as its privacy risk assessment tool have resolved the challenges privacy practitioners were previously facing in implementing PIA. To do so, our methodology is based on comparison and thematic analysis on two sets of focus groups we held with privacy professionals back in January 2018 (four months before GDPR came into effect) and then in November 2019 (18 months after GDPR implementation
Open Access
Designing Strong Privacy Metrics Suites Using Evolutionary Optimization
(ACM, 2021) Wagner, Isabel; Yevseyeva, Iryna
The ability to measure privacy accurately and consistently is key in the development of new privacy protections. However, recent studies have uncovered weaknesses in existing privacy metrics, as well as weaknesses caused by the use of only a single privacy metric. Metrics suites, or combinations of privacy metrics, are a promising mechanism to alleviate these weaknesses, if we can solve two open problems: which metrics should be combined, and how. In this paper, we tackle the first problem, i.e. the selection of metrics for strong metrics suites, by formulating it as a knapsack optimization problem with both single and multiple objectives. Because solving this problem exactly is difficult due to the large number of combinations and many qualities/objectives that need to be evaluated for each metrics suite, we apply 16 existing evolutionary and metaheuristic optimization algorithms. We solve the optimization problem for three privacy application domains: genomic privacy, graph privacy, and vehicular communications privacy. We find that the resulting metrics suites have better properties, i.e. higher monotonicity, diversity, evenness, and shared value range, than previously proposed metrics suites.
Open Access
Dying of a Hundred Good Symptoms: Why Good Security Can Still Fail - A Literature Review and Analysis
(Taylor & Francis, 2019-04-05) Loft, Paul; He, Ying; Janicke, Helge; Wagner, Isabel
Many organizations suffer serious information security incidents, despite having taken positive steps towards achieving good security standards. Security certifications and high levels of maturity may have been obtained, but fundamental security problems remain. The authors hypothesize that these issues are often as a result of security arrangements not being sufficiently integrated with how the whole organization actually goes about its business. Whether embarking on a new Enterprise Information System (EIS) or refreshing a security strategy, we believe that adopting an enterprise architecture (EA) approach to implementing information security – commonly referred to as an ‘Enterprise Information Security Architecture’ (EISA) - will deliver substantial benefits. However, EAs typically require specialist resources to develop and maintain them, and this takes time; which makes it difficult for architectures to keep pace with business change. These barriers must be overcome if the EISA is to be effective. Our paper has reviewed and analyzed literature concerning the root causes of information security incidents and describes a novel approach for ensuring that the most critical factors are considered when building an EISA framework. We propose 8 domains that must be managed together to ensure that an EISA is successful.
Open Access
Evaluating the Strength of Genomic Privacy Metrics
(ACM, 2017-02-06) Wagner, Isabel
The genome is a unique identifier for human individuals. The genome also contains highly sensitive information, creating a high potential for misuse of genomic data (for example, genetic discrimination). In this paper, we investigate how genomic privacy can be measured in scenarios where an adversary aims to infer a person’s genomic markers by constructing probability distributions on the values of genetic variations. We measured the strength of privacy metrics by requiring that metrics are monotonic with increasing adversary strength and uncovered serious problems with several existing metrics currently used to measure genomic privacy. We provide suggestions on metric selection, interpretation, and visualization, and illustrate the work flow using case studies for three real-world diseases.
Embargo
Gender and Performance in Computer Science
(ACM, 2016-06-27) Wagner, Isabel
The term gender gap refers to the significant underrepresentation of females in many subjects. In Computer Science, the gender gap exists at all career levels. In this paper, we study whether there is a performance gap in addition to the gender gap. To answer this question, we analyzed statistical data on student performance in Computer Science from 129 universities in the UK covering the years 2002 to 2013. We find that male students were awarded significantly more first-class degrees than female students. We evaluate four other subjects – Subjects Allied to Medicine, Business & Administrative Studies, Mathematical Sciences, and Engineering & Technology – and find that they do not exhibit this performance gap. Following on from this finding, we review explanations for the gender and performance gaps, as well as potential solutions to eliminate the gaps. Most solutions do not require major institutional change and could thus be implemented easily.
Open Access
How Location-Aware Access Control Affects User Privacy and Security in Cloud Computing Systems
(EAI, 2020-06-10) Zeng, W.; Bashir, Reem; Wood, Trevor; Siewe, Francois; Janicke, Helge; Wagner, Isabel
The use of cloud computing (CC) is rapidly increasing due to the demand for internet services and communications. The large number of services and data stored in the cloud creates security risks due to the dynamic movement of data, connected devices and users between various cloud environments. In this study, we will develop an innovative prototype for location-aware access control and data privacy for CC systems. We will apply location-aware access control policies to role-based access control of Cloud Foundry, and then analyze the impact on user privacy after implementing these policies. This innovation can be used to address the security risks introduced by inter-cloud use and communication, and will have significant impact in making citizen’s personal data more secure.
Open Access
Measuring Privacy in Vehicular Networks
(IEEE, 2017-11-16) Wagner, Isabel
Vehicular communication plays a key role in near- future automotive transport, promising features like increased traffic safety or wireless software updates. However, vehicular communication can expose driver locations and thus poses important privacy risks. Many schemes have been proposed to protect privacy in vehicular communication, and their effectiveness is usually shown using privacy metrics. However, to the best of our knowledge, (1) different privacy metrics have never been compared to each other, and (2) it is unknown how strong the metrics are. In this paper, we argue that privacy metrics should be monotonic, i.e. that they indicate decreasing privacy for increasing adversary strength, and we evaluate the monotonicity of 32 privacy metrics on real and synthetic traffic with state-of- the-art adversary models. Our results indicate that most privacy metrics are weak at least in some situations. We therefore recommend to use metrics suites, i.e. combinations of privacy metrics, when evaluating new privacy-enhancing technologies.
Open Access
A Novel Principle to Validate Digital Forensic Models
(Elsevier, 2020-03-10) Mothi, Dinesh; Janicke, Helge; Wagner, Isabel
Digital forensic models (DFMs) form the base for any digital investigation because they guide the investigators with necessary steps and procedures to be taken during the investigation. State-of-the-art DFMs assume that it is safe to proceed from one stage of the investigation to the next without taking into account the anti-forensic techniques that could be used to defeat the investigation process.However, the findings in the literature shows that common phases in the digital forensic process such as acquisition, examination, analysis, and reporting are affected by various anti- forensic (AF) methods.To fill this gap, we propose an abstract digital forensic framework and validate DFMs by factoring in AF techniques affecting various phases in a digital forensic process. This validation principle can be used to enhance state-of-the-art DFMs to enable principled detection and countering of AF techniques before being applied to a real-time investigation case.
Open Access
On the Strength of Privacy Metrics for Vehicular Communication
(IEEE, 2018-05-03) Zhao, Yuchen; Wagner, Isabel
Embargo
POSTER: Design Ideas for Privacy-aware User Interfaces for Mobile Devices
(ACM, 2016-07) Tailor, Neel; He, Ying; Wagner, Isabel
Open Access
POSTER: Evaluating Privacy Metrics for Graph Anonymization and De-anonymization
(ACM, 2018-06) Zhao, Y.; Wagner, Isabel
Many modern communication systems generate graph data, for example social networks and email networks. Such graph data can be used for recommender systems and data mining. However, because graph data contains sensitive information about individuals, sharing or publishing graph data may pose privacy risks. To protect graph privacy, data anonymization has been proposed to prevent individual users in a graph from being identified by adversaries. The effectiveness of both anonymization and de-anonymization techniques is usually evaluated using the adversary’s success rate. However, the success rate does not measure privacy for individual users in a graph because it is an aggregate per-graph metric. In addition, it is unclear whether the success rate is monotonic, i.e. whether it indicates higher privacy for weaker adversaries, and lower privacy for stronger adversaries. To address these gaps, we propose a methodology to systematically evaluate the monotonicity of graph privacy metrics, and present preliminary results for the monotonicity of 25 graph privacy metrics.
Open Access
Privacy in the Smart City - Applications, Technologies, Challenges and Solutions
(IEEE, 2017-09-05) Eckhoff, David; Wagner, Isabel
Many modern cities strive to integrate information technology into every aspect of city life to create so-called smart cities. Smart cities rely on a large number of application areas and technologies to realize complex interactions between citizens, third parties, and city departments. This overwhelming complexity is one reason why holistic privacy protection only rarely enters the picture. A lack of privacy can result in discrimination and social sorting, creating a fundamentally unequal society. To prevent this, we believe that a better understanding of smart cities and their privacy implications is needed. We therefore systematize the application areas, enabling technologies, privacy types, attackers and data sources for the attacks, giving structure to the fuzzy term “smart city”. Based on our taxonomies, we describe existing privacy-enhancing technologies, review the state of the art in real cities around the world, and discuss promising future research directions. Our survey can serve as a reference guide, contributing to the development of privacy-friendly smart cities.
Open Access
Privacy Risk Assessment: From Art to Science, by Metrics
(Springer, 2018-09-07) Wagner, Isabel; Boiten, Eerke Albert
Privacy risk assessments aim to analyze and quantify the privacy risks associated with new systems. As such, they are critically important in ensuring that adequate privacy protections are built in. However, current methods to quantify privacy risk rely heavily on experienced analysts picking the “correct” risk level on e.g. a five-point scale. In this paper, we argue that a more scientific quantification of privacy risk increases accuracy and reliability and can thus make it easier to build privacy-friendly systems. We discuss how the impact and likelihood of privacy violations can be decomposed and quantified, and stress the importance of meaningful metrics and units of measurement. We suggest a method of quantifying and representing privacy risk that considers a collection of factors as well as a variety of contexts and attacker models. We conclude by identifying some of the major research questions to take this approach further in a variety of application scenarios.
Open Access
Risks and benefits of smart toilets
(ACM, 2023-11-15) Wagner, Isabel; Boiten, Eerke Albert
Smart toilets promise convenient 24/7 health and wellness monitoring. However, privacy risks of smart toilets have not been carefully studied. Here, we present a thematic analysis of an expert focus group on smart toilets that record health data. The themes indicate severe privacy and systemic risks, many of which could be mitigated but currently are not. Our analysis suggests that health benefits outweigh risks only in specific application contexts.
Open Access
Security and Privacy in Unified Communication
(ACM, 2022-02-03) Reisinger, Thomas; Wagner, Isabel; Boiten, Eerke Albert
The use of unified communication; video conferencing, audio conferencing, and instant messaging has skyrocketed during the COVID-19 pandemic. However, security and privacy considerations have often been neglected. This paper provides a comprehensive survey of security and privacy in Unified Communication (UC). We systematically analyze security and privacy threats and mitigations in a generic UC scenario. Based on this, we analyze security and privacy features of the major UC market leaders and we draw conclusions on the overall UC landscape. While confidentiality in communication channels is generally well protected through encryption, other privacy properties are mostly lacking on UC platforms.
Open Access
Social internet of vehicles for smart cities
(Multidisciplinary Digital Publishing Institute, 2016-01) Maglaras, Leandros; Al-Bayatti, Ali Hilal; He, Ying; Wagner, Isabel; Janicke, Helge
Open Access
Technical Privacy Metrics: a Systematic Survey
(ACM Digital LIbrary, 2018-07-16) Wagner, Isabel; Eckhoff, David
The goal of privacy metrics is to measure the degree of privacy enjoyed by users in a system and the amount of protection offered by privacy-enhancing technologies. In this way, privacy metrics contribute to improving user privacy in the digital world. The diversity and complexity of privacy metrics in the literature makes an informed choice of metrics challenging. As a result, instead of using existing metrics, new metrics are proposed frequently, and privacy studies are often incomparable. In this survey we alleviate these problems by structuring the landscape of privacy metrics. To this end, we explain and discuss a selection of over eighty privacy metrics and introduce categorizations based on the aspect of privacy they measure, their required inputs, and the type of data that needs protection. In addition, we present a method on how to choose privacy metrics based on nine questions that help identify the right privacy metrics for a given scenario, and highlight topics where additional work on privacy metrics is needed. Our survey spans multiple privacy domains and can be understood as a general framework for privacy measurement.