Democracy, Ideology and Process Re-Engineering: Realising the Benefits of e-Government in Singapore

The re-engineering of governmental processes is a necessary condition for the realisation of the benefits of e-government. Several obstacles to such re-engineering exist. These include: (1) information processing thrives on transparency and amalgamation of data, whilst governments are constrained by principles of privacy and data separation; (2) top-down re-engineering may be resisted effectively from the bottom up. This paper analyses these obstacles in the way of re-engineering in Singapore – a democratic one-party state where legislative and executive power lies with the People’s Action Party – and considers how that hegemony has aided the development of e-government

A Hybrid Approach for Data Analytics for Internet of Things

The vision of the Internet of Things is to allow currently unconnected physical objects to be connected to the internet. There will be an extremely large number of internet connected devices that will be much more than the number of human being in the world all producing data. These data will be collected and delivered to the cloud for processing, especially with a view of finding meaningful information to then take action. However, ideally the data needs to be analysed locally to increase privacy, give quick responses to people and to reduce use of network and storage resources. To tackle these problems, distributed data analytics can be proposed to collect and analyse the data either in the edge or fog devices. In this paper, we explore a hybrid approach which means that both innetwork level and cloud level processing should work together to build effective IoT data analytics in order to overcome their respective weaknesses and use their specific strengths. Specifically, we collected raw data locally and extracted features by applying data fusion techniques on the data on resource constrained devices to reduce the data and then send the extracted features to the cloud for processing. We evaluated the accuracy and data consumption over network and thus show that it is feasible to increase privacy and maintain accuracy while reducing data communication demands.Comment: Accepted to be published in the Proceedings of the 7th ACM International Conference on the Internet of Things (IoT 2017

Privacy and security of consumer IoT devices for the pervasive monitoring of vulnerable people

The Internet of Things (IoT) promises highly innovative solutions to a wide range of activities. However, simply being a technology company does not exempt an IoT company from needing to comply with the legislation applicable to their operating region that safeguards personal information. This will result in security and privacy requirements for healthcare solutions. There are several mature frameworks that address these issues, but they have been developed within the context of organised hospitals and care providers, where there is the expertise, processing power, communications and electrical power to support highly robust security. However, for IoT solutions aimed at vulnerable people, either at home or within their local environment, there are significant additional constraints that must be overcome. These include technical (low processing capability, power constrained, intermittent communications) organisational (how to enrol and revoke users and devices, distribution of cryptographic keys) and user constraints (how does a patient with physical and/or mental challenges configure and update their devices). This paper considers at the legal frameworks and the security and privacy requirements for healthcare solutions. An overview of some of the primary frameworks is then provided followed by an assessment of how this is constrained within an IoT system

Enabling Privacy and Trust in Edge AI Systems

Recent advances in mobile computing and the Internet of Things (IoT) enable the global integration of heterogeneous smart devices via wireless networks. A common characteristic across these modern day systems is their ability to collect and communicate streaming data, making machine learning (ML) appealing for processing, reasoning, and predicting about the environment. More recently, low network latency requirements have made offloading intelligence to the cloud undesirable. These novel requirements have led to the emergence of edge computing, an approach that brings computation closer to the device with low latency, high throughput, and enhanced reliability. Together, they enable ML-powered information processing and control pipelines spanning end devices, edge computing, and cloud environments. However, continuous collaboration between cloud, edge and device is susceptible to information leakage and loss, leading to insecure and unreliable operation. This raises an important question: how can we design, develop, and evaluate high-performing ML systems that are trustworthy and privacy-preserving in resource-constrained edge environments? In this thesis, I address this question by designing and implementing privacy-preserving and trustworthy ML systems for distributed applications. I first introduce a system that establishes trust in the explanations generated from a popular visualization technique, saliency maps, using counterfactual reasoning. Through the proposed evaluation system, I assess the degree to which hypothesized explanations correspond to the semantics of edge-based reinforcement learning environments. Second, I examine the privacy implications of personalized models in distributed mobile services by proposing time-series based model inversion attacks. To thwart such attacks, I present a distributed framework, Pelican, that learns and deploys transfer learning-based personalized ML models in a privacy preserving manner on resource-constrained mobile devices. Third, I investigate ML models that are deployed on local devices for inference and highlight the ease with which proprietary information embedded in these models can be exposed. For mitigating such attacks, I present a secure on-device application framework, SODA, which is supported by real-time adversarial detection. Finally, I present an end-to-end privacy-aware system for a real-world application to model group interaction behavior via mobility sensing. The proposed system, W4-Groups, distributes computation across device, edge, and cloud resources to strengthen its privacy and trustworthiness guarantees

DeepSecure: Scalable Provably-Secure Deep Learning

This paper proposes DeepSecure, a novel framework that enables scalable execution of the state-of-the-art Deep Learning (DL) models in a privacy-preserving setting. DeepSecure targets scenarios in which neither of the involved parties including the cloud servers that hold the DL model parameters or the delegating clients who own the data is willing to reveal their information. Our framework is the first to empower accurate and scalable DL analysis of data generated by distributed clients without sacrificing the security to maintain efficiency. The secure DL computation in DeepSecure is performed using Yao's Garbled Circuit (GC) protocol. We devise GC-optimized realization of various components used in DL. Our optimized implementation achieves more than 58-fold higher throughput per sample compared with the best-known prior solution. In addition to our optimized GC realization, we introduce a set of novel low-overhead pre-processing techniques which further reduce the GC overall runtime in the context of deep learning. Extensive evaluations of various DL applications demonstrate up to two orders-of-magnitude additional runtime improvement achieved as a result of our pre-processing methodology. This paper also provides mechanisms to securely delegate GC computations to a third party in constrained embedded settings

PAgIoT - Privacy-preserving aggregation protocol for internet of things

Modern society highly relies on the use of cyberspace to perform a huge variety of activities, such as social networking or e-commerce, and new technologies are continuously emerging. As such, computer systems may store a huge amount of information, which makes data analysis and storage a challenge. Information aggregation and correlation are two basic mechanisms to reduce the problem size, for example by filtering out redundant data or grouping similar one. These processes require high processing capabilities, and thus their application in Internet of Things (IoT) scenarios is not straightforward due to resource constraints. Furthermore, privacy issues may arise when the data at stake is personal. In this paper we propose PAgIoT, a Privacy-preserving Aggregation protocol suitable for IoT settings. It enables multi-attribute aggregation for groups of entities while allowing for privacy-preserving value correlation. Results show that PAgIoT is resistant to security attacks, it outperforms existing proposals that provide with the same security features, and it is feasible in resource-constrained devices and for aggregation of up to 10 attributes in big networks.This work was partially supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You) and the CAM grant S2013/ICE-3095 CIBERDINE-CM (CIBERDINE: Cybersecurity, Data, and Risks) funded by the Autonomous Community of Madrid and co-funded by European funds