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Bao, Shihan, Lei, Ao, Cruickshank, Haitham, Sun, Zhili, Asuquo, Philip, Hathal, Waleed.  2019.  A Pseudonym Certificate Management Scheme Based on Blockchain for Internet of Vehicles. 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :28–35.
Research into the established area of ITS is evolving into the Internet of Vehicles (IoV), itself a fast-moving research area, fuelled in part by rapid changes in computing and communication technologies. Using pseudonym certificate is a popular way to address privacy issues in IoV. Therefore, the certificate management scheme is considered as a feasible technique to manage system and maintain the lifecycle of certificate. In this paper, we propose an efficient pseudonym certificate management scheme in IoV. The Blockchain concept is introduced to simplify the network structure and distributed maintenance of the Certificate Revocation List (CRL). The proposed scheme embeds part of the certificate revocation functions within the security and privacy applications, aiming to reduce the communication overhead and shorten the processing time cost. Extensive simulations and analysis show the effectiveness and efficiency of the proposed scheme, in which the Blockchain structure costs fewer network resources and gives a more economic solution to against further cybercrime attacks.
Peng, Ruxiang, Li, Weishi, Yang, Tao, Huafeng, Kong.  2019.  An Internet of Vehicles Intrusion Detection System Based on a Convolutional Neural Network. 2019 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom). :1595–1599.
With the continuous development of the Internet of Vehicles, vehicles are no longer isolated nodes, but become a node in the car network. The open Internet will introduce traditional security issues into the Internet of Things. In order to ensure the safety of the networked cars, we hope to set up an intrusion detection system (IDS) on the vehicle terminal to detect and intercept network attacks. In our work, we designed an intrusion detection system for the Internet of Vehicles based on a convolutional neural network, which can run in a low-powered embedded vehicle terminal to monitor the data in the car network in real time. Moreover, for the case of packet encryption in some car networks, we have also designed a separate version for intrusion detection by analyzing the packet header. Experiments have shown that our system can guarantee high accuracy detection at low latency for attack traffic.
Indira, K, Ajitha, P, Reshma, V, Tamizhselvi, A.  2019.  An Efficient Secured Routing Protocol for Software Defined Internet of Vehicles. 2019 International Conference on Computational Intelligence in Data Science (ICCIDS). :1–4.
Vehicular ad hoc network is one of most recent research areas to deploy intelligent Transport System. Due to their highly dynamic topology, energy constrained and no central point coordination, routing with minimal delay, minimal energy and maximize throughput is a big challenge. Software Defined Networking (SDN) is new paradigm to improve overall network lifetime. It incorporates dynamic changes with minimal end-end delay, and enhances network intelligence. Along with this, intelligence secure routing is also a major constraint. This paper proposes a novel approach to Energy efficient secured routing protocol for Software Defined Internet of vehicles using Restricted Boltzmann Algorithm. This algorithm is to detect hostile routes with minimum delay, minimum energy and maximum throughput compared with traditional routing protocols.
Sharma, Sachin, Ghanshala, Kamal Kumar, Mohan, Seshadri.  2019.  Blockchain-Based Internet of Vehicles (IoV): An Efficient Secure Ad Hoc Vehicular Networking Architecture. 2019 IEEE 2nd 5G World Forum (5GWF). :452–457.
With the transformation of connected vehicles into the Internet of Vehicles (IoV), the time is now ripe for paving the way for the next generation of connected vehicles with novel applications and innovative security measures. The connected vehicles are experiencing prenominal growth in the auto industry, but are still studded with many security and privacy vulnerabilities. Today's IoV applications are part of cyber physical communication systems that collect useful information from thousands of smart sensors associated with the connected vehicles. The technology advancement has paved the way for connected vehicles to share significant information among drivers, auto manufacturers, auto insurance companies and operational and maintenance service providers for various applications. The critical issues in engineering the IoV applications are effective to use of the available spectrum and effective allocation of good channels an opportunistic manner to establish connectivity among vehicles, and the effective utilization of the infrastructure under various traffic conditions. Security and privacy in information sharing are the main concerns in a connected vehicle communication network. Blockchain technology facilitates secured communication among users in a connected vehicles network. Originally, blockchain technology was developed and employed with the cryptocurrency. Bitcoin, to provide increased trust, reliability, and security among users based on peer-to-peer networks for transaction sharing. In this paper, we propose to integrate blockchain technology into ad hoc vehicular networking so that the vehicles can share network resources with increased trust, reliability, and security using distributed access control system and can benefit a wider scope of scalable IoV applications scenarios for decision making. The proposed architecture is the faithful environment for information sharing among connected vehicles. Blockchain technology allows multiple copies of data storage at the distribution cloud. Distributed access control system is significantly more secure than a traditional centralized system. This paper also describes how important of ad hoc vehicular networking in human life, possibilities in real-world implementation and its future trends. The ad hoc vehicular networking may become one of the most trendy networking concepts in the future that has the perspective to bring out much ease human beneficial and secured applications.
Hasan, Khondokar Fida, Kaur, Tarandeep, Hasan, Md. Mhedi, Feng, Yanming.  2019.  Cognitive Internet of Vehicles: Motivation, Layered Architecture and Security Issues. 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI). :1–6.
Over the past few years, we have experienced great technological advancements in the information and communication field, which has significantly contributed to reshaping the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect data, the data exchanged paradigm among vehicles is shifted from the local network to the cloud. With the introduction of cloud and edge computing along with ubiquitous 5G mobile network, it is expected to see the role of Artificial Intelligence (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge of industrial revolution 4.0, it is necessary first of all to get a clear understanding of the cutting-edge technologies that going to take place in the automotive ecosystem so that the cyber-physical impact on transportation system can be measured. CIoV, which is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in transportation, and it has amassed great attention. It introduces cloud-based artificial intelligence and machine learning into transportation system. What are the future expectations of CIoV? To fully contemplate this architecture's future potentials, and milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet the security requirements of its practical implementation. Aiming to that, this paper presents the evolution of CIoV along with the layer abstractions to outline the distinctive functional parts of the proposed architecture. It also gives an investigation of the prime security and privacy issues associated with technological evolution to take measures.
Dong, Hongbo, Zhu, Qianxiang.  2019.  A Cyber-Physical Interaction Model Based Impact Assessment of Cyberattacks for Internet of Vehicles. 2019 4th International Conference on Communication and Information Systems (ICCIS). :79–83.
Internet of Vehicles are the important part of Intelligence Traffic Systems (ITS), which are essential for the national security and economy development. The impact assessment for cyberattacks in the IoV protection is of great theoretical and practical significance. Most of the researchers in this field pay attention on the attack impact on a vehicle, and the seldom investigate the impact on the whole system which combines all the vehicles as a whole integration. To tackle this problem, a cyber-physical interaction model based impact assessment of cyberattacks is presented. In this approach, the operation of IoV is modeled from the cyberphysical interaction perspective, and then the propagating process from cyber layer to physical layer is investigated. Based on above model, the impact assessment of cyberattacks on IoV is realized quantitatively. Finally, a simulation on an IoV is conducted to verify the effectiveness of this approach.
Engoulou, Richard Gilles, Bellaiche, Martine, Halabi, Talal, Pierre, Samuel.  2019.  A Decentralized Reputation Management System for Securing the Internet of Vehicles. 2019 International Conference on Computing, Networking and Communications (ICNC). :900–904.
The evolution of the Internet of Vehicles (IoV) paradigm has recently attracted a lot of researchers and industries. Vehicular Ad Hoc Networks (VANET) is the networking model that lies at the heart of this technology. It enables the vehicles to exchange relevant information concerning road conditions and safety. However, ensuring communication security has been and still is one of the main challenges to vehicles' interconnection. To secure the interconnected vehicular system, many cryptography techniques, communication protocols, and certification and reputation-based security approaches were proposed. Nonetheless, some limitations are still present, preventing the practical implementation of such approaches. In this paper, we first define a set of locally-perceived behavioral reputation parameters that enable a distributed evaluation of vehicles' reputation. Then, we integrate these parameters into the design of a reputation management system to exclude malicious or faulty vehicles from the IoV network. Our system can help in the prevention of several attacks on the VANET environment such as Sybil and Denial of Service attacks, and can be implemented in a fully decentralized fashion.
Aladwan, Mohammad, Awaysheh, Feras, Cabaleiro, José, Pena, Tomás, Alabool, Hamzeh, Alazab, Mamoun.  2019.  Common Security Criteria for Vehicular Clouds and Internet of Vehicles Evaluation and Selection. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :814–820.
Internet of Things (IoT) is becoming increasingly important to intelligent transportation system stakeholders, including cloud-based vehicular cloud (VC) and internet of vehicles (IoV) paradigms. This new trend involves communication and data exchange between several objects within different layers of control. Security in such a deployment is pivotal to realize the general IoT-based smart city. However, the evaluation of the degree of security regarding these paradigms remains a challenge. This study aims to discover and identify common security criteria (CSC) from a context-based analysis pattern and later to discuss, compare, and aggregate a conceptual model of CSC impartially. A privacy granularity classification that maintains data confidentiality is proposed alongside the security selection criteria.
Kő, Andrea, Molnár, Tamás, Mátyus, Bálint.  2018.  A User-centred Design Approach for Mobile- Government Systems for the Elderly. 2018 12th International Conference on Software, Knowledge, Information Management Applications (SKIMA). :1—7.

This paper aims to discover the characteristics of acceptance of mobile government systems by elderly. Several initiatives and projects offer various governmental services for them, like information sharing, alerting and mHealth services. All of them carry important benefits for this user group, but these can only be utilized if the user acceptance is at a certain level. This is a requirement in order for the users to perceive the services as a benefit and not as hindrance. The key aspects for high acceptance are usability and user-friendliness, which will lead to successful-government systems designed for the target group. We have applied a combination of qualitative and quantitative research methods including an m-Government prototype to explore the key acceptance factors. Research approach utilizes the IGUAN framework, which is a user-driven method. We collected and analysed data guided by IGUAN framework about the acceptance of e-government services by elderly. The target group was recruited from Germany and Hungary. Our findings draw the attention to perceived security and perceived usability of an application; these are decisive factors for this target group.

Marchand-Niño, William-Rogelio, Fonseca, Bruno Paolo Guzman.  2019.  Social Engineering for Diagnostic the Information Security Culture. 2019 IEEE 39th Central America and Panama Convention (CONCAPAN XXXIX). :1–6.
In the process of diagnosing the culture of information security in an organization, it is considered two methods, the first one is the application of an ISCA (Information Security Culture Assessment) survey questionnaire and the second one based on social engineering techniques such as phishing, answering the question, How can a diagnosis be made effectively of the level of information security culture within an organization? with the objective of determining which of the two methods is the most effective and realistic for the diagnosis of the information security culture. This helps to understand and have a real and complete perception of the behavior and reaction of the users against the attacks of threat actors who make use of persuasion and manipulation tactics in order to obtain confidential or sensitive information. A description of these two methods is applied to a case study (public university). As a result, it is obtained that it is not enough to perform a diagnosis based on questionnaires because they can be relatively subjective in the sense of the way in which users respond to questions or statements. Evidence of controlled social engineering attacks that demonstrate in more detail the real behavior of users should be considered. Based on this more complete knowledge, appropriate strategies can be formulated for the change or strengthening of the security culture that ultimately contributes to the purpose of protecting information assets.
Flores, Pedro, Farid, Munsif, Samara, Khalid.  2019.  Assessing E-Security Behavior among Students in Higher Education. 2019 Sixth HCT Information Technology Trends (ITT). :253–258.
This study was conducted in order to assess the E-security behavior of students in a large higher educational institutions in the United Arab Emirates (UAE). Specifically, it sought to determine the current state of students' E-security behavior in the aspects of malware, password usage, data handling, phishing, social engineering, and online scam. An E- Security Behavior Survey Instrument (EBSI) was used to determine the status of security behavior of the participants in doing their computing activities. To complement the survey tool, focus group discussions were conducted to elicit specific experiences and insights of the participants relative to E-security. The results of the study shows that the overall E-security behavior among students in higher education in the United Arab Emirates (UAE) is moderately favorable. Specifically, the investigation reveals that the students favorably behave when it comes to phishing, social engineering, and online scam. However, they uncertainly behave on malware issues, password usage, and data handling.
Ferguson-Walter, Kimberly, Major, Maxine, Van Bruggen, Dirk, Fugate, Sunny, Gutzwiller, Robert.  2019.  The World (of CTF) is Not Enough Data: Lessons Learned from a Cyber Deception Experiment. 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC). :346–353.
The human side of cyber is fundamentally important to understanding and improving cyber operations. With the exception of Capture the Flag (CTF) exercises, cyber testing and experimentation tends to ignore the human attacker. While traditional CTF events include a deeply rooted human component, they rarely aim to measure human performance, cognition, or psychology. We argue that CTF is not sufficient for measuring these aspects of the human; instead, we examine the value in performing red team behavioral and cognitive testing in a large-scale, controlled human-subject experiment. In this paper we describe the pros and cons of performing this type of experimentation and provide detailed exposition of the data collection and experimental controls used during a recent cyber deception experiment-the Tularosa Study. Finally, we will discuss lessons learned and how our experiences can inform best practices in future cyber operations studies of human behavior and cognition.
Jeong, Jongkil, Mihelcic, Joanne, Oliver, Gillian, Rudolph, Carsten.  2019.  Towards an Improved Understanding of Human Factors in Cybersecurity. 2019 IEEE 5th International Conference on Collaboration and Internet Computing (CIC). :338–345.
Cybersecurity cannot be addressed by technology alone; the most intractable aspects are in fact sociotechnical. As a result, the 'human factor' has been recognised as being the weakest and most obscure link in creating safe and secure digital environments. This study examines the subjective and often complex nature of human factors in the cybersecurity context through a systematic literature review of 27 articles which span across technical, behavior and social sciences perspectives. Results from our study suggest that there is still a predominately a technical focus, which excludes the consideration of human factors in cybersecurity. Our literature review suggests that this is due to a lack of consolidation of the attributes pertaining to human factors; the application of theoretical frameworks; and a lack of in-depth qualitative studies. To ensure that these gaps are addressed, we propose that future studies take into consideration (a) consolidating the human factors; (b) examining cyber security from an interdisciplinary approach; (c) conducting additional qualitative research whilst investigating human factors in cybersecurity.
Sánchez, Marco, Torres, Jenny, Zambrano, Patricio, Flores, Pamela.  2018.  FraudFind: Financial fraud detection by analyzing human behavior. 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC). :281–286.
Financial fraud is commonly represented by the use of illegal practices where they can intervene from senior managers until payroll employees, becoming a crime punishable by law. There are many techniques developed to analyze, detect and prevent this behavior, being the most important the fraud triangle theory associated with the classic financial audit model. In order to perform this research, a survey of the related works in the existing literature was carried out, with the purpose of establishing our own framework. In this context, this paper presents FraudFind, a conceptual framework that allows to identify and outline a group of people inside an banking organization who commit fraud, supported by the fraud triangle theory. FraudFind works in the approach of continuous audit that will be in charge of collecting information of agents installed in user's equipment. It is based on semantic techniques applied through the collection of phrases typed by the users under study for later being transferred to a repository for later analysis. This proposal encourages to contribute with the field of cybersecurity, in the reduction of cases of financial fraud.
Kautsarina, Anggorojati, Bayu.  2018.  A Conceptual Model for Promoting Positive Security Behavior in Internet of Things Era. 2018 Global Wireless Summit (GWS). :358–363.
As the Internet of Things (IoT) era raise, billions of additional connected devices in new locations and applications will create new challenges. Security and privacy are among the major challenges in IoT as any breaches and misuse in those aspects will have the adverse impact on users. Among many factors that determine the security of any system, human factor is the most important aspect to be considered; as it is renowned that human is the weakest link in the information security cycle. Experts express the need to increase cyber resilience culture and a focus on the human factors involved in cybersecurity to counter cyber risks. The aim of this study is to propose a conceptual model to improve cyber resilience in IoT users that is adapted from a model in public health sector. Cyber resilience is improved through promoting security behavior by gathering the existing knowledge and gain understanding about every contributing aspects. The proposed approach is expected to be used as foundation for government, especially in Indonesia, to derive strategies in improving cyber resilience of IoT users.
Alissa, Khalid Adnan, Alshehri, Hanan Abdullah, Dahdouh, Shahad Abdulaziz, Alsubaie, Basstaa Mohammad, Alghamdi, Afnan Mohammed, Alharby, Abdulrahman, Almubairik, Norah Ahmed.  2018.  An Instrument to Measure Human Behavior Toward Cyber Security Policies. 2018 21st Saudi Computer Society National Computer Conference (NCC). :1–6.
Human is the weakest link in information security. Even with strong cyber security policies an organization can still be hacked because of a human error. Even if people are aware of the policies and their importance they might not behave accordingly. This shows to the importance of studying and measuring human behavior toward cyber security policies. This paper introduces a new instrument that can be used to measure human behavior toward cybersecurity policies through creative measures. The goal is to gather data about human behaviors toward cybersecurity policies in natural environment. This method of gathering information allows people to behave normally and don't feel the need to answer perfectly. The paper illustrates all the previous work related to the subject, summarizing previous work in order to improve what have been previously done. The methodology seeks on measuring behavior based on specific measures. These measures are the password, email, identity, sensitive data, and physical/resource security. Each measure has a number of policies used to measure behavior. These policies were selected among several policies based on literature from the same field and the opinion of experts in the field. These question that went through several rounds of check were used to build the proposed-instrument. This instrument then shall be used by researchers to collect data and perform the required analysis. This paper discusses the behavior pattern in a detail and concise manner. The paper demonstrates that it is posable to measure behavior if the right we questions were asked in the right way.
Chowdhury, Noman H., Adam, Marc T. P., Skinner, Geoffrey.  2018.  The Impact of Time Pressure on Human Cybersecurity Behavior: An Integrative Framework. 2018 26th International Conference on Systems Engineering (ICSEng). :1–10.
Cybersecurity is a growing concern for private individuals and professional entities. Thereby, reports have shown that the majority of cybersecurity incidents occur because users fail to behave securely. Research on human cybersecurity (HCS) behavior suggests that time pressure is one of the important driving factors behind insecure HCS behavior. However, as our review reveals, studies on the role of time pressure in HCS are scant and there is no framework that can inform researchers and practitioners on this matter. In this paper, we present a conceptual framework consisting of contexts, psychological constructs, and boundary conditions pertaining to the role time pressure plays on HCS behavior. The framework is also validated and extended by findings from semi-structured interviews of different stakeholder groups comprising of cybersecurity experts, professionals, and general users. The framework will serve as a guideline for future studies exploring different aspects of time pressure in cybersecurity contexts and also to identify potential countermeasures for the detrimental impact of time pressure on HCS behavior.
Marrone, Stefano, Sansone, Carlo.  2019.  An Adversarial Perturbation Approach Against CNN-based Soft Biometrics Detection. 2019 International Joint Conference on Neural Networks (IJCNN). :1–8.
The use of biometric-based authentication systems spread over daily life consumer electronics. Over the years, researchers' interest shifted from hard (such as fingerprints, voice and keystroke dynamics) to soft biometrics (such as age, ethnicity and gender), mainly by using the latter to improve the authentication systems effectiveness. While newer approaches are constantly being proposed by domain experts, in the last years Deep Learning has raised in many computer vision tasks, also becoming the current state-of-art for several biometric approaches. However, since the automatic processing of data rich in sensitive information could expose users to privacy threats associated to their unfair use (i.e. gender or ethnicity), in the last years researchers started to focus on the development of defensive strategies in the view of a more secure and private AI. The aim of this work is to exploit Adversarial Perturbation, namely approaches able to mislead state-of-the-art CNNs by injecting a suitable small perturbation over the input image, to protect subjects against unwanted soft biometrics-based identification by automatic means. In particular, since ethnicity is one of the most critical soft biometrics, as a case of study we will focus on the generation of adversarial stickers that, once printed, can hide subjects ethnicity in a real-world scenario.
Granatyr, Jones, Gomes, Heitor Murilo, Dias, João Miguel, Paiva, Ana Maria, Nunes, Maria Augusta Silveira Netto, Scalabrin, Edson Emílio, Spak, Fábio.  2019.  Inferring Trust Using Personality Aspects Extracted from Texts. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). :3840–3846.
Trust mechanisms are considered the logical protection of software systems, preventing malicious people from taking advantage or cheating others. Although these concepts are widely used, most applications in this field do not consider affective aspects to aid in trust computation. Researchers of Psychology, Neurology, Anthropology, and Computer Science argue that affective aspects are essential to human's decision-making processes. So far, there is a lack of understanding about how these aspects impact user's trust, particularly when they are inserted in an evaluation system. In this paper, we propose a trust model that accounts for personality using three personality models: Big Five, Needs, and Values. We tested our approach by extracting personality aspects from texts provided by two online human-fed evaluation systems and correlating them to reputation values. The empirical experiments show statistically significant better results in comparison to non-personality-wise approaches.
Khayat, Mohamad, Barka, Ezedin, Sallabi, Farag.  2019.  SDN\_Based Secure Healthcare Monitoring System(SDN-SHMS). 2019 28th International Conference on Computer Communication and Networks (ICCCN). :1–7.
Healthcare experts and researchers have been promoting the need for IoT-based remote health monitoring systems that take care of the health of elderly people. However, such systems may generate large amounts of data, which makes the security and privacy of such data to become imperative. This paper studies the security and privacy concerns of the existing Healthcare Monitoring System (HMS) and proposes a reference architecture (security integration framework) for managing IoT-based healthcare monitoring systems that ensures security, privacy, and reliable service delivery for patients and elderly people to reduce and avoid health related risks. Our proposed framework will be in the form of state-of-the-art Security Platform, for HMS, using the emerging Software Defined Network (SDN) networking paradigm. Our proposed integration framework eliminates the dependency on specific Software or vendor for different security systems, and allows for the benefits from the functional and secure applications, and services provided by the SDN platform.
MacMahon, Silvana Togneri, Alfano, Marco, Lenzitti, Biagio, Bosco, Giosuè Lo, McCaffery, Fergal, Taibi, Davide, Helfert, Markus.  2019.  Improving Communication in Risk Management of Health Information Technology Systems by means of Medical Text Simplification. 2019 IEEE Symposium on Computers and Communications (ISCC). :1135–1140.
Health Information Technology Systems (HITS) are increasingly used to improve the quality of patient care while reducing costs. These systems have been developed in response to the changing models of care to an ongoing relationship between patient and care team, supported by the use of technology due to the increased instance of chronic disease. However, the use of HITS may increase the risk to patient safety and security. While standards can be used to address and manage these risks, significant communication problems exist between experts working in different departments. These departments operate in silos often leading to communication breakdowns. For example, risk management stakeholders who are not clinicians may struggle to understand, define and manage risks associated with these systems when talking to medical professionals as they do not understand medical terminology or the associated care processes. In order to overcome this communication problem, we propose the use of the “Three Amigos” approach together with the use of the SIMPLE tool that has been developed to assist patients in understanding medical terms. This paper examines how the “Three Amigos” approach and the SIMPLE tool can be used to improve estimation of severity of risk by non-clinical risk management stakeholders and provides a practical example of their use in a ten step risk management process.
Chia, Pern Hui, Desfontaines, Damien, Perera, Irippuge Milinda, Simmons-Marengo, Daniel, Li, Chao, Day, Wei-Yen, Wang, Qiushi, Guevara, Miguel.  2019.  KHyperLogLog: Estimating Reidentifiability and Joinability of Large Data at Scale. 2019 IEEE Symposium on Security and Privacy (SP). :350–364.
Understanding the privacy relevant characteristics of data sets, such as reidentifiability and joinability, is crucial for data governance, yet can be difficult for large data sets. While computing the data characteristics by brute force is straightforward, the scale of systems and data collected by large organizations demands an efficient approach. We present KHyperLogLog (KHLL), an algorithm based on approximate counting techniques that can estimate the reidentifiability and joinability risks of very large databases using linear runtime and minimal memory. KHLL enables one to measure reidentifiability of data quantitatively, rather than based on expert judgement or manual reviews. Meanwhile, joinability analysis using KHLL helps ensure the separation of pseudonymous and identified data sets. We describe how organizations can use KHLL to improve protection of user privacy. The efficiency of KHLL allows one to schedule periodic analyses that detect any deviations from the expected risks over time as a regression test for privacy. We validate the performance and accuracy of KHLL through experiments using proprietary and publicly available data sets.
Puspitaningrum, Diyah, Fernando, Julio, Afriando, Edo, Utama, Ferzha Putra, Rahmadini, Rina, Pinata, Y..  2019.  Finding Local Experts for Dynamic Recommendations Using Lazy Random Walk. 2019 7th International Conference on Cyber and IT Service Management (CITSM). 7:1–6.
Statistics based privacy-aware recommender systems make suggestions more powerful by extracting knowledge from the log of social contacts interactions, but unfortunately, they are static - moreover, advice from local experts effective in finding specific business categories in a particular area. We propose a dynamic recommender algorithm based on a lazy random walk that recommends top-rank shopping places to potentially interested visitors. We consider local authority and topical authority. The algorithm tested on FourSquare shopping data sets of 5 cities in Indonesia with k-steps=5,7,9 (lazy) random walks and compared the results with other state-of-the-art ranking techniques. The results show that it can reach high score precisions (0.5, 0.37, and 0.26 respectively on p@1, p@3, and p@5 for k=5). The algorithm also shows scalability concerning execution time. The advantage of dynamicity is the database used to power the recommender system; no need to be very frequently updated to produce a good recommendation.
D'Angelo, Mirko, Gerasimou, Simos, Ghahremani, Sona, Grohmann, Johannes, Nunes, Ingrid, Pournaras, Evangelos, Tomforde, Sven.  2019.  On Learning in Collective Self-Adaptive Systems: State of Practice and a 3D Framework. 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). :13–24.
Collective self-adaptive systems (CSAS) are distributed and interconnected systems composed of multiple agents that can perform complex tasks such as environmental data collection, search and rescue operations, and discovery of natural resources. By providing individual agents with learning capabilities, CSAS can cope with challenges related to distributed sensing and decision-making and operate in uncertain environments. This unique characteristic of CSAS enables the collective to exhibit robust behaviour while achieving system-wide and agent-specific goals. Although learning has been explored in many CSAS applications, selecting suitable learning models and techniques remains a significant challenge that is heavily influenced by expert knowledge. We address this gap by performing a multifaceted analysis of existing CSAS with learning capabilities reported in the literature. Based on this analysis, we introduce a 3D framework that illustrates the learning aspects of CSAS considering the dimensions of autonomy, knowledge access, and behaviour, and facilitates the selection of learning techniques and models. Finally, using example applications from this analysis, we derive open challenges and highlight the need for research on collaborative, resilient and privacy-aware mechanisms for CSAS.
Foreman, Zackary, Bekman, Thomas, Augustine, Thomas, Jafarian, Haadi.  2019.  PAVSS: Privacy Assessment Vulnerability Scoring System. 2019 International Conference on Computational Science and Computational Intelligence (CSCI). :160–165.
Currently, the guidelines for business entities to collect and use consumer information from online sources is guided by the Fair Information Practice Principles set forth by the Federal Trade Commission in the United States. These guidelines are inadequate, outdated, and provide little protection for consumers. Moreover, there are many techniques to anonymize the stored data that was collected by large companies and governments. However, what does not exist is a framework that is capable of evaluating and scoring the effects of this information in the event of a data breach. In this work, a framework for scoring and evaluating the vulnerability of private data is presented. This framework is created to be used in parallel with currently adopted frameworks that are used to score and evaluate other areas of deficiencies within the software, including CVSS and CWSS. It is dubbed the Privacy Assessment Vulnerability Scoring System (PAVSS) and quantifies the privacy-breach vulnerability an individual takes on when using an online platform. This framework is based on a set of hypotheses about user behavior, inherent properties of an online platform, and the usefulness of available data in performing a cyber attack. The weight each of these metrics has within our model is determined by surveying cybersecurity experts. Finally, we test the validity of our user-behavior based hypotheses, and indirectly our model by analyzing user posts from a large twitter data set.