arXiv subjects

Aruna Seneviratne

Publications and source records attributed to Aruna Seneviratne.

49 records · Page 3Linked to original sources

arXiv2020

Conservative Plane Releasing for Spatial Privacy Protection in Mixed Reality

Augmented reality (AR) or mixed reality (MR) platforms require spatial understanding to detect objects or surfaces, often including their structural (i.e. spatial geometry) and photometric (e.g. color, and texture) attributes, to allow applications to place virtual or synthetic objects seemingly "anchored" on to real world objects; in some cases, even allowing interactions between the physical and virtual objects. These functionalities require AR/MR platforms to capture the 3D spatial information with high resolution and frequency; however, these pose unprecedented risks to user privacy. Aside from objects being detected, spatial information also reveals the location of the user with high specificity, e.g. in which part of the house the user is. In this work, we propose to leverage spatial generalizations coupled with conservative releasing to provide spatial privacy while maintaining data utility. We designed an adversary that builds up on existing place and shape recognition methods over 3D data as attackers to which the proposed spatial privacy approach can be evaluated against. Then, we simulate user movement within spaces which reveals more of their space as they move around utilizing 3D point clouds collected from Microsoft HoloLens. Results show that revealing no more than 11 generalized planes--accumulated from successively revealed spaces with large enough radius, i.e. $r\leq1.0m$--can make an adversary fail in identifying the spatial location of the user for at least half of the time. Furthermore, if the accumulated spaces are of smaller radius, i.e. each successively revealed space is $r\leq 0.5m$, we can release up to 29 generalized planes while enjoying both better data utility and privacy.

cs.CV↗

arXiv2019

Characterizing and Detecting Money Laundering Activities on the Bitcoin Network

Bitcoin is by far the most popular crypto-currency solution enabling peer-to-peer payments. Despite some studies highlighting the network does not provide full anonymity, it is still being heavily used for a wide variety of dubious financial activities such as money laundering, ponzi schemes, and ransom-ware payments. In this paper, we explore the landscape of potential money laundering activities occurring across the Bitcoin network. Using data collected over three years, we create transaction graphs and provide an in-depth analysis on various graph characteristics to differentiate money laundering transactions from regular transactions. We found that the main difference between laundering and regular transactions lies in their output values and neighbourhood information. Then, we propose and evaluate a set of classifiers based on four types of graph features: immediate neighbours, curated features, deepwalk embeddings, and node2vec embeddings to classify money laundering and regular transactions. Results show that the node2vec-based classifier outperforms other classifiers in binary classification reaching an average accuracy of 92.29% and an F1-measure of 0.93 and high robustness over a 2.5-year time span. Finally, we demonstrate how effective our classifiers are in discovering unknown laundering services. The classifier performance dropped compared to binary classification, however, the prediction can be improved with simple ensemble techniques for some services.

cs.SI↗

arXiv2019

Blockchain as a Service for Multi-Access Edge Computing: A Deep Reinforcement Learning Approach

Recently, blockchain has gained momentum in the academic community thanks to its decentralization, immutability, transparency and security. As an emerging paradigm, Multi-access Edge Computing (MEC) has been widely used to provide computation and storage resources to mobile user equipments (UE) at the edge of the network for improving the performance of mobile applications. In this paper, we propose a novel blockchain-based MEC architecture where UEs can offload their computation tasks to the MEC servers. In particular, a blockchain network is deployed and hosted on the MEC platform as Blockchain as a Service (BaaS) that supports smart contract-based resource trading and transaction mining services for mobile task offloading. To enhance the performance of the blockchain-empowered MEC system, we propose a joint scheme of computation offloading and blockchain mining. Accordingly, an optimization problem is formulated to maximize edge service revenue and blockchain mining reward while minimizing the service computation latency with respect to constraints of user service demands and hash power resource. We then propose a novel Deep Reinforcement Learning (DRL) approach using a double deep Q-network (DQN) algorithm to solve the proposed problem. Numerical results demonstrate that the proposed scheme outperforms the other baseline methods in terms of better system utility with computational efficiency. Experiment results also verify that the trading contract design is efficient with low operation cost, showing the feasibility of the proposed scheme.

cs.NI↗

arXiv2019

Blockchain for 5G and Beyond Networks: A State of the Art Survey

The fifth generation (5G) wireless networks are on the way to be deployed around the world. The 5G technologies target to support diverse vertical applications by connecting heterogeneous devices and machines with drastic improvements in terms of high quality of service, increased network capacity and enhanced system throughput. Despite all these advantages that 5G will bring about, there are still major challenges to be addressed, including decentralization, transparency, risks of data interoperability, network privacy and security vulnerabilities. Blockchain can offer innovative solutions to effectively solve the challenges in 5G networks. Driven by the dramatically increased capacities of the 5G networks and the recent breakthroughs in the blockchain technology, blockchain-based 5G services are expected to witness a rapid development and bring substantial benefits to future society. In this paper, we provide a state-of-art survey on the integration of blockchain with 5G networks and beyond. Our key focus is on the discussions on the potential of blockchain for enabling key 5G technologies, including cloud/edge computing, Software Defined Networks, Network Function Virtualization, Network Slicing, and D2D communications. We then explore the opportunities of blockchain to important 5G services, ranging from spectrum management, network virtualization, resource management to interference management, federated learning, privacy and security provision. The recent advances in the applications of blockchain in 5G Internet of Things are also surveyed in various domains, i.e. smart healthcare, smart city, smart transportation, smart grid and UAVs. The main findings derived from the survey are then summarized, and possible research challenges with open issues are also identified. Lastly, we complete this survey by shedding new light on future directions of research on this newly emerging area.

cs.NI↗

arXiv2019

Blockchain-based Smart Contracts - Applications and Challenges

A blockchain-based smart contract or a "smart contract" for short, is a computer program intended to digitally facilitate the negotiation or contractual terms directly between users when certain conditions are met. With the advance in blockchain technology, smart contracts are being used to serve a wide range of purposes ranging from self-managed identities on public blockchains to automating business collaboration on permissioned blockchains. In this paper, we present a comprehensive survey of smart contracts with a focus on existing applications and challenges they face.

cs.CY↗

arXiv2019

H2B: Heartbeat-based Secret Key Generation Using Piezo Vibration Sensors

We present Heartbeats-2-Bits (H2B), which is a system for securely pairing wearable devices by generating a shared secret key from the skin vibrations caused by heartbeat. This work is motivated by potential power saving opportunity arising from the fact that heartbeat intervals can be detected energy-efficiently using inexpensive and power-efficient piezo sensors, which obviates the need to employ complex heartbeat monitors such as Electrocardiogram or Photoplethysmogram. Indeed, our experiments show that piezo sensors can measure heartbeat intervals on many different body locations including chest, wrist, waist, neck and ankle. Unfortunately, we also discover that the heartbeat interval signal captured by piezo vibration sensors has low Signal-to-Noise Ratio (SNR) because they are not designed as precision heartbeat monitors, which becomes the key challenge for H2B. To overcome this problem, we first apply a quantile function-based quantization method to fully extract the useful entropy from the noisy piezo measurements. We then propose a novel Compressive Sensing-based reconciliation method to correct the high bit mismatch rates between the two independently generated keys caused by low SNR. We prototype H2B using off-the-shelf piezo sensors and evaluate its performance on a dataset collected from different body positions of 23 participants. Our results show that H2B has an overwhelming pairing success rate of 95.6%. We also analyze and demonstrate H2B's robustness against three types of attacks. Finally, our power measurements show that H2B is very power-efficient.

cs.CR↗

arXiv2018

Virtualized Application Function Chaining: Maximizing the Wearable System Lifetime

The number of smart devices wear and carry by users is growing rapidly which is driven by innovative new smart wearables and interesting service o erings. This has led to applications that utilize multiple devices around the body to provide immersive environments such as mixed reality. These applications rely on a number of di erent types of functions such as sensing, communication and various types of processing, that require considerable resources. Thus one of the major challenges in supporting of these applications is dependent on the battery lifetime of the devices that provide the necessary functionality. The battery lifetime can be extended by either incorporating a battery with larger capacity and/or by utilizing the available resources e ciently. However, the increases in battery capacity are not keeping up with the demand and larger batteries add to both the weight and size of the device. Thus, the focus of this paper is to improve the battery e ciency through intelligent resources utilization. We show that, when the same resource is available on multiple devices that form part of the wearable system, and or is in close proximity, it is possible consider them as a resource pool and further utilize them intelligently to improve the system lifetime. Speci cally, we formulate the function allocation algorithm as a Mixed Integer Linear Programming (MILP) optimization problem and propose an e cient heuristic solution. The experimental data driven simulation results show that approximately 40-50% system battery life improvement can be achieved with proper function allocation and orchestration.

cs.NI↗

arXiv2018

A Delay-Tolerant Payment Scheme Based on the Ethereum Blockchain

Banking as an essential service can be hard to access in remote, rural regions where the network connectivity is intermittent. Although micro-banking has been made possible by SMS or USSD messages in some places, their security flaws and session-based nature prevent them from a wider adoption. Global level cryptocurrencies enable low-cost, secure and pervasive money transferring among distributed peers, but are still limited in their ability to reach more people in remote communities. We proposed to take advantage of the delay-tolerant nature of blockchains to deliver banking services to remote communities that only connect to the broader Internet intermittently. Using a base station that offers connectivity within the local area, regular transaction processing is solely handled by blockchain miners. The bank only joins to process currency exchange requests, reward miners and track user balances when the connection is available. By distributing the verification and storage tasks among peers, our system design saves on the overall deployment and operational costs without sacrificing the reliability and trustwor- thiness. Through theoretical and empirical analysis, we provided insights to system design, tested its robustness against network disturbances, and demonstrated the feasibility of implementation on off-the-shelf computers and mobile devices.

cs.CY↗

arXiv2017

Seamless Resources Sharing in Wearable Networks by Application Function Virtualization

The prevalence of smart wearable devices is increasing exponentially and we are witnessing a wide variety of fascinating new services that leverage the capabilities of these wearables. Wearables are truly changing the way mobile computing is deployed and mobile applications are being developed. It is possible to leverage the capabilities such as connectivity, processing, and sensing of wearable devices in an adaptive manner for efficient resource usage and information accuracy within the personal area network. We show that application developers are not yet taking advantage of these cross-device capabilities, however, instead using wearables as passive sensors or simple end displays to provide notifications to the user. We thus design AFV (Application Function Virtualization), an architecture enabling automated dynamic function virtualization and scheduling across devices in a personal area network, simplifying the development of the apps that are adaptive to context changes. AFV provides a simple set of APIs hiding complex architectural tasks from app developers whilst continuously monitoring the user, device and network context, to enable the adaptive invocation of functions across devices. We show the feasibility of our design by implementing AFV on Android, and the benefits for the user in terms of resource efficiency, especially in saving energy consumption, and quality of experience with multiple use cases.

cs.OS↗

arXiv2017

BreathRNNet: Breathing Based Authentication on Resource-Constrained IoT Devices using RNNs

Recurrent neural networks (RNNs) have shown promising results in audio and speech processing applications due to their strong capabilities in modelling sequential data. In many applications, RNNs tend to outperform conventional models based on GMM/UBMs and i-vectors. Increasing popularity of IoT devices makes a strong case for implementing RNN based inferences for applications such as acoustics based authentication, voice commands, and edge analytics for smart homes. Nonetheless, the feasibility and performance of RNN based inferences on resources-constrained IoT devices remain largely unexplored. In this paper, we investigate the feasibility of using RNNs for an end-to-end authentication system based on breathing acoustics. We evaluate the performance of RNN models on three types of devices; smartphone, smartwatch, and Raspberry Pi and show that unlike CNN models, RNN models can be easily ported onto resource-constrained devices without a significant loss in accuracy.

cs.CR↗

arXiv2017

uStash: a Novel Mobile Content Delivery System for Improving User QoE in Public Transport

Mobile data traffic is growing exponentially and it is even more challenging to distribute content efficiently while users are "on the move" such as in public transport.The use of mobile devices for accessing content (e.g. videos) while commuting are both expensive and unreliable, although it is becoming common practice worldwide. Leveraging on the spatial and temporal correlation of content popularity and users' diverse network connectivity, we propose a novel content distribution system, \textit{uStash}, which guarantees better QoE with regards to access delays and cost of usage. The proposed collaborative download and content stashing schemes provide the uStash provider the flexibility to control the cost of content access via cellular networks. We model the uStash system in a probabilistic framework and thereby analytically derive the optimal portions for collaborative downloading. Then, we validate the proposed models using real-life trace driven simulations. In particular, we use dataset from 22 inter-city buses running on 6 different routes and from a mobile VoD service provider to show that uStash reduces the cost of monthly cellular data by approximately 50\% and the expected delay for content access by 60\% compared to content downloaded via users' cellular network connections.

cs.NI↗

arXiv2016

Are wearable devices ready for HTTPS? Measuring the cost of secure communication protocols on wearable devices

The majority of available wearable devices require communication with Internet servers for data analysis and storage, and rely on a paired smartphone to enable secure communication. However, wearable devices are mostly equipped with WiFi network interfaces, enabling direct communication with the Internet. Secure communication protocols should then run on these wearables itself, yet it is not clear if they can be efficiently supported. In this paper, we show that wearable devices are ready for direct and secure Internet communication by means of experiments with both controlled and Internet servers. We observe that the overall energy consumption and communication delay can be reduced with direct Internet connection via WiFi from wearables compared to using smartphones as relays via Bluetooth. We also show that the additional HTTPS cost caused by TLS handshake and encryption is closely related to number of parallel connections, and has the same relative impact on wearables and smartphones.

cs.CR↗

arXiv2006

Multicasting with selective delivery: A SafetyNet for vertical handoffs

In future mobility support will require handling roaming in heterogeneous access networks. In order to enable seamless roaming it is necessary to minimize the impact of the vertical handoffs. Localized mobility management schemes such as FMIPv6 and HMIPv6 do not provide sufficient handoff performance, since they have been designed for horizontal handoffs. In this paper, we propose the SafetyNet protocol, which allows a Mobile Node to perform seamless vertical handoffs. Further, we propose a handoff timing algorithm which allows a Mobile Node to delay or even completely avoid upward vertical handoffs. We implement the SafetyNet protocol and compare its performance with the Fast Handovers for Mobile IPv6 protocol in our wireless test bed and analyze the results. The experimental results indicate that the proposed SafetyNet protocol can provide an improvement of up to 95% for TCP performance in vertical handoffs, when compared with FMIPv6 and an improvement of 64% over FMIPv6 with bicasting. We use numerical analysis of the protocol to show that its signaling and data transmission overhead is comparable to Fast Mobile IPv6 and significantly smaller than that of FMIPv6 with bicasting.

cs.NI↗