arXiv subjects

Charith Perera

Publications and source records attributed to Charith Perera.

At least 37 records · Page 2Linked to original sources

arXiv2020

Synthesising Privacy by Design Knowledge Towards Explainable Internet of Things Application Designing in Healthcare

Privacy by Design (PbD) is the most common approach followed by software developers who aim to reduce risks within their application designs, yet it remains commonplace for developers to retain little conceptual understanding of what is meant by privacy. A vision is to develop an intelligent privacy assistant to whom developers can easily ask questions in order to learn how to incorporate different privacy-preserving ideas into their IoT application designs. This paper lays the foundations toward developing such a privacy assistant by synthesising existing PbD knowledge so as to elicit requirements. It is believed that such a privacy assistant should not just prescribe a list of privacy-preserving ideas that developers should incorporate into their design. Instead, it should explain how each prescribed idea helps to protect privacy in a given application design context-this approach is defined as 'Explainable Privacy'. A total of 74 privacy patterns were analysed and reviewed using ten different PbD schemes to understand how each privacy pattern is built and how each helps to ensure privacy. Due to page limitations, we have presented a detailed analysis in [3]. In addition, different real-world Internet of Things (IoT) use-cases, including a healthcare application, were used to demonstrate how each privacy pattern could be applied to a given application design. By doing so, several knowledge engineering requirements were identified that need to be considered when developing a privacy assistant. It was also found that, when compared to other IoT application domains, privacy patterns can significantly benefit healthcare applications. In conclusion, this paper identifies the research challenges that must be addressed if one wishes to construct an intelligent privacy assistant that can truly augment software developers' capabilities at the design phase.

cs.SE↗

arXiv2020

Cyberattacks and Countermeasures For In-Vehicle Networks

As connectivity between and within vehicles increases, so does concern about safety and security. Various automotive serial protocols are used inside vehicles such as Controller Area Network (CAN), Local Interconnect Network (LIN) and FlexRay. CAN bus is the most used in-vehicle network protocol to support exchange of vehicle parameters between Electronic Control Units (ECUs). This protocol lacks security mechanisms by design and is therefore vulnerable to various attacks. Furthermore, connectivity of vehicles has made the CAN bus not only vulnerable from within the vehicle but also from outside. With the rise of connected cars, more entry points and interfaces have been introduced on board vehicles, thereby also leading to a wider potential attack surface. Existing security mechanisms focus on the use of encryption, authentication and vehicle Intrusion Detection Systems (IDS), which operate under various constrains such as low bandwidth, small frame size (e.g. in the CAN protocol), limited availability of computational resources and real-time sensitivity. We survey In-Vehicle Network (IVN) attacks which have been grouped under: direct interfaces-initiated attacks, telematics and infotainment-initiated attacks, and sensor-initiated attacks. We survey and classify current cryptographic and IDS approaches and compare these approaches based on criteria such as real time constrains, types of hardware used, changes in CAN bus behaviour, types of attack mitigation and software/ hardware used to validate these approaches. We conclude with potential mitigation strategies and research challenges for the future.

cs.CR↗

arXiv2020

Privacy in Data Service Composition

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this paper, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives.

cs.DB↗

arXiv2019

Exploring the Suitability of BLE Beacons to Track Poacher Vehicles in Harsh Jungle Terrains

Our overall aim is focused on exploring whether we could use Bluetooth Low Energy (BLE) technology to track poacher vehicles in remote and rural areas such as Sabah, in Malaysia, especially deep inside the jungle terrain with little or no communication technologies exists. Tracking technologies are currently limited to relying on satellites or cellular towers, for environments that do not permit access to these signals, very few viable alternatives exist. This paper explores the use of BLE as a method to track vehicles. It works by mounting Bluetooth beacons beside a road and placing a receiver concealed somewhere inside the vehicle. As the vehicle drives past the beacon, the receiver and beacon are momentarily in range, the receiver then stores a unique ID from the beacon and when the vehicle is then in an area with GSM signal, an SMS is sent containing the unique IDs of the beacons that have been detected. This project is prototyped and tested in collaboration with the Danau Girang Field Centre in Sabah, Malaysia. The results offer insights for how effective BLE beacons are in a tracking situation for where the beacon and receiver are in range for a short period of time as well as how different obstructions will affect the range and strength of the signal. It is important to note that our objective is not to catch the poacher, instead to understand how they move around within jungle terrain, as we can use such information to develop a comprehensive plan against poaching activities.

cs.NI↗

arXiv2019

Context Aware Family Dynamics based Internet of Things Access Control Towards Better Child Safety

Today, children are increasingly connected to the Internet and consume content and services through various means. It has been a challenge for less tech-savvy parents to protect children from harmful content and services. Internet of Things (IoT) has made the situation much worse as IoT devices allow children to connect to the Internet in novel ways (e.g., connected refrigerators, TVs, and so on). In this paper, we propose mySafeHome, an approach which utilises family dynamics to provide a more natural, and intuitive access control mechanism to protect children from harmful content and services in the context of IoT. In mySafeHome, access control dynamically adapts based on the physical distance between family members. For example, a particular type of content can only be consumed, through TV, by children if the parents are in the same room (or hearing distance). mySafeHome allows parents to assess a given content by themselves. Our approach also aims to create granular levels of access control (e.g., block / limit certain content, features, services, on certain devices when the parents are not in the vicinity). We developed a prototype using OpenHAB and several smart home devices to demonstrate the proposed approach. We believe that our approach also facilitates the creation of better relationships between family members. A demo can be viewed here: http://safehome.technology/demo.

cs.HC↗

arXiv2019

Designing Security and Privacy Requirements in Internet of Things: A Survey

The design and development process for the Internet of Things (IoT) applications is more complicated than that for desktop, mobile, or web applications. First, IoT applications require both software and hardware to work together across different nodes with different capabilities under different conditions. Secondly, IoT application development involves different software engineers such as desktop, web, embedded and mobile to cooperate. In addition, the development process required different software\hardware stacks to integrated together. Due to above complexities, more often non-functional requirements (such as security and privacy) tend to get ignored in IoT application development process. In this paper, we have reviewed techniques, methods and tools that are being developed to support incorporating security and privacy requirements into traditional application designs. By doing so, we aim to explore how those techniques could be applicable to the IoT domain. In this paper, we primarily focused on two different aspects: (1) design notations, models, and languages that facilitate capturing non-functional requirements (i.e., security and privacy), and (2) proactive and reactive interaction techniques that can be used to support and augment the IoT application design process. Our goal is not only to analyse past research work but also to discuss their applicability towards the IoT.

cs.SE↗

arXiv2019

IoT Skullfort: Exploring the Impact of Internet Connected Cosplay

In this paper, we explore the potential impact of Internet of Things (IoT) technology may have on the cosplay community. We developed a costume (an IoT Skullfort) and embedded IoT technology to enhance its capabilities and user interactions. Sensing technologies are widely used in many different wearable domains including cosplay scenarios. However, in most of these scenarios, typical interaction pattern is that the costume responds to its environment or the player's behaviour (e.g., colour of lights may get changed when player moves hands). In contrast, our research focuses on exploring scenarios where the audience (third party) get to manipulate the costume behaviour (e.g., the audience get to change the colour of the Skullfort using a mobile application). We believe such an audience (third party) influenced cosplay brings new opportunities for enhanced entertainment. However, it also creates significant challenges. We report the results gathered through a focus group conducted in collaboration with cosplay community experts.

cs.HC↗

arXiv2019

PizzaBox: Studying Internet Connected Physical Object Manipulation based Food Ordering

This paper presents the designing and testing of PizzaBox, a 3D printed, interactive food ordering system that aims to differ from conventional food ordering systems and provide an entertaining and unique experience when ordering a pizza by incorporating underlying technologies that support ubiquitous computing. The PizzaBox has gone through both low and medium fidelity testing while working collaboratively with participants to co-design and refine a product that is approachable to all age groups while maintaining a simple process for ordering food from start to finish. Final testing was conducted at an independent pizzeria where interviews with participants lead us to develop four discussion themes 1) usability and end user engagement, 2) towards connected real-time products and services, 3) healthy eating, 4) evolution of food ordering systems. Our interviews show that in general, PizzaBox would have a greater appeal to a younger audience by providing a fantasy of helping in the creation and baking of the pizza but also has a novelty value that all ages would enjoy. We investigate the effect that the PizzaBox has in encouraging new healthy habits or promoting a healthier lifestyle as well as how we can improve PizzaBox to better encourage these lifestyle changes.

cs.HC↗

arXiv2019

Crowdsourced Peer Learning Activity for Internet of Things Education: A Case Study

Computing devices such as laptops, tablets and mobile phones have become part of our daily lives. End users increasingly know more and more information about these devices. Further, more technically savvy end users know how such devices are being built and know how to choose one over the others. However, we cannot say the same about the Internet of Things (IoT) products. Due to its infancy nature of the marketplace, end users have very little idea about IoT products. To address this issue, we developed a method, a crowdsourced peer learning activity, supported by an online platform (OLYMPUS) to enable a group of learners to learn IoT products space better. We conducted two different user studies to validate that our tool enables better IoT education. Our method guide learners to think more deeply about IoT products and their design decisions. The learning platform we developed is open source and available for the community.

cs.HC↗

arXiv2019

A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks

In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink). This algorithm can reproduce the data that have not been sampled by finding the spatial and temporal correlation among the reported data set, and filling the 'non-sampled' parts with predictions. We have used real sensor data of a network that was deployed at the Grand-St-Bernard pass located between Switzerland and Italy. We tested our approach using the previously mentioned data-set and compared it to a recent adaptive sampling based data reduction approach. The obtained results show that our proposed method consumes up to 60% less energy and can handle non-stationary data more effectively.

cs.NI↗

arXiv2019

Designing Privacy-aware Internet of Things Applications

Internet of Things (IoT) applications typically collect and analyse personal data that can be used to derive sensitive information about individuals. However, thus far, privacy concerns have not been explicitly considered in software engineering processes when designing IoT applications. The advent of behaviour driven security mechanisms, failing to address privacy concerns in the design of IoT applications can have security implications. In this paper, we explore how a Privacy-by-Design (PbD) framework, formulated as a set of guidelines, can help software engineers integrate data privacy considerations into the design of IoT applications. We studied the utility of this PbD framework by studying how software engineers use it to design IoT applications. We also explore the challenges in using the set of guidelines to influence the IoT applications design process. In addition to highlighting the benefits of having a PbD framework to make privacy features explicit during the design of IoT applications, our studies also surfaced a number of challenges associated with the approach. A key finding of our research is that the PbD framework significantly increases both novice and expert software engineers' ability to design privacy into IoT applications.

cs.SE↗

arXiv2019

The Role of Big Data Analytics in Industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well.

cs.CY↗

arXiv2019

Designing Sensing as a Service (S2aaS) Ecosystem for Internet of Things

The Internet of Things (IoT) envisions the creation of an environment where everyday objects (e.g. microwaves, fridges, cars, coffee machines, etc.) are connected to the internet and make users' lives more productive, efficient, and convenient. During this process, everyday objects capture a vast amount of data that can be used to understand individuals and their behaviours. In the current IoT ecosystems, such data is collected and used only by the respective IoT solutions. There is no formal way to share data with external entities. We believe this is very efficient and unfair for users. We believe that users, as data owners, should be able to control, manage, and share data about them in any way that they choose and make or gain value out of them. To achieve this, we proposed the Sensing as a Service (S2aaS) model. In this paper, we discuss the Sensing as a Service ecosystem in terms of its architecture, components and related user interaction designs. This paper aims to highlight the weaknesses of the current IoT ecosystem and to explain how S2aaS would eliminate those weaknesses. We also discuss how an everyday user may engage with the S2aaS ecosystem and design challenges.

cs.NI↗

arXiv2019

Exploring the Effectiveness of Service Decomposition in Fog Computing Architecture for the Internet of Things

The Internet of Things (IoT) aims to connect everyday physical objects to the internet. These objects will produce a significant amount of data. The traditional cloud computing architecture aims to process data in the cloud. As a result, a significant amount of data needs to be communicated to the cloud. This creates a number of challenges, such as high communication latency between the devices and the cloud, increased energy consumption of devices during frequent data upload to the cloud, high bandwidth consumption, while making the network busy by sending the data continuously, and less privacy because of less control on the transmitted data to the server. Fog computing has been proposed to counter these weaknesses. Fog computing aims to process data at the edge and substantially eliminate the necessity of sending data to the cloud. However, combining the Service Oriented Architecture (SOA) with the fog computing architecture is still an open challenge. In this paper, we propose to decompose services to create linked-microservices (LMS). Linked-microservices are services that run on multiple nodes but closely linked to their linked-partners. Linked-microservices allow distributing the computation across different computing nodes in the IoT architecture. Using four different types of architectures namely cloud, fog, hybrid and fog+cloud, we explore and demonstrate the effectiveness of service decomposition by applying four experiments to three different type of datasets. Evaluation of the four architectures shows that decomposing services into nodes reduce the data consumption over the network by 10% - 70%. Overall, these results indicate that the importance of decomposing services in the context of fog computing for enhancing the quality of service.

cs.NI↗

arXiv2019

Deterrence and Prevention-based Model to Mitigate Information Security Insider Threats in Organisations

Previous studies show that information security breaches and privacy violations are important issues for organisations and people. It is acknowledged that decreasing the risk in this domain requires consideration of the technological aspects of information security alongside human aspects. Employees intentionally or unintentionally account for a significant portion of the threats to information assets in organisations. This research presents a novel conceptual framework to mitigate the risk of insiders using deterrence and prevention approaches. Deterrence factors discourage employees from engaging in information security misbehaviour in organisations, and situational crime prevention factors encourage them to prevent information security misconduct. Our findings show that perceived sanctions certainty and severity significantly influence individuals' attitudes and deter them from information security misconduct. In addition, the output revealed that increasing the effort, risk and reducing the reward (benefits of crime) influence the employees' attitudes towards prevent information security misbehaviour. However, removing excuses and reducing provocations do not significantly influence individuals' attitudes towards prevent information security misconduct. Finally, the output of the data analysis also showed that subjective norms, perceived behavioural control and attitude influence individuals' intentions, and, ultimately, their behaviour towards avoiding information security misbehaviour.

cs.CY↗

arXiv2018

Hybrid Microaggregation for Privacy-Preserving Data Mining

k-Anonymity by microaggregation is one of the most commonly used anonymization techniques. This success is owe to the achievement of a worth of interest tradeoff between information loss and identity disclosure risk. However, this method may have some drawbacks. On the disclosure limitation side, there is a lack of protection against attribute disclosure. On the data utility side, dealing with a real datasets is a challenging task to achieve. Indeed, the latter are characterized by their large number of attributes and the presence of noisy data, such that outliers or, even, data with missing values. Generating an anonymous individual data useful for data mining tasks, while decreasing the influence of noisy data is a compelling task to achieve. In this paper, we introduce a new microaggregation method, called HM-PFSOM, based on fuzzy possibilistic clustering. Our proposed method operates through an hybrid manner. This means that the anonymization process is applied per block of similar data. Thus, we can help to decrease the information loss during the anonymization process. The HMPFSOM approach proposes to study the distribution of confidential attributes within each sub-dataset. Then, according to the latter distribution, the privacy parameter k is determined, in such a way to preserve the diversity of confidential attributes within the anonymized microdata. This allows to decrease the disclosure risk of confidential information.

cs.CR↗

arXiv2018

Data-driven Air Quality Characterisation for Urban Environments: a Case Study

The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and the resultant datasets also featuring missing labels, computational data-driven mechanisms are needed to address the data sparsity challenge. In this paper, we propose a machine learning-based method to accurately predict the Air Quality Index (AQI), using environmental monitoring data together with meteorological measurements. To do so, we develop an air quality estimation framework that implements a neural network that is enhanced with a novel Non-linear Autoregressive neural network with exogenous input (NARX), especially designed for time series prediction. The framework is applied to a case study featuring different monitoring sites in London, with comparisons against other standard machine-learning based predictive algorithms showing the feasibility and robust performance of the proposed method for different kinds of areas within an urban region.

cs.CY↗

arXiv2018

User-centric Privacy Engineering for the Internet of Things

User privacy concerns are widely regarded as a key obstacle to the success of modern smart cyber-physical systems. In this paper, we analyse, through an example, some of the requirements that future data collection architectures of these systems should implement to provide effective privacy protection for users. Then, we give an example of how these requirements can be implemented in a smart home scenario. Our example architecture allows the user to balance the privacy risks with the potential benefits and take a practical decision determining the extent of the sharing. Based on this example architecture, we identify a number of challenges that must be addressed by future data processing systems in order to achieve effective privacy management for smart cyber-physical systems.

cs.NI↗