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"The adoption of data-driven applications across economic sectors has made data and the flow of data so pervasive that it has become integral to everything we as members of society do - from conducting our finances to operating businesses to powering the apps we use every day. Flows of knowledge and technology are at the centre of new networks driving production and innovation. The increasing use of the Internet of Things (IoT), and the growing amount of data generated, are driving substantial opportunities. Data is now one of the world's most valuable resources, and its flow across borders is the lifeblood of the global internet economy. Data has already significantly impacted various industrial sectors - e.g. trade, banking and finance, telecommunications, media/entertainment and healthcare - and the global economy overall. Governing cross-border data flows is inherently difficult given the ubiquity and value of data, and the impact government policies can have on national competitiveness, business attractiveness and personal rights. The challenge for governments is to address in a coherent manner the broad range of data-related issues in the context of a global data-driven economy. While larger economies such as the US, EU and China have clear policies and overarching objectives in place, many smaller jurisdictions have yet to adopt a strategy or framework. This is regrettable, as it is imperative that all jurisdictions have a clear strategy on cross-border data which is designed to meet the opportunities and challenges of the digital transformation. Instead, many jurisdictions currently operate on a "by default" combination of piecemeal legislation and obligations undertaken in free trade agreements. This book engages with the unexplored topic of why and how governments should develop a coherent and consistent framework regulating cross-border data flows. The objective is to fill a very significant gap in the legal and policy setting by considering multiple perspectives in order to assist in the development of a jurisdiction's coherent policy framework"--

AbstractThe Internet and the free flow of data across borders is becoming a key platform for international trade. Digital products can be sold online and the Internet provides opportunities for business to use the Internet to manage global supply chains, communicate with customers and access IT in the cloud. At the same time, governments are restricting the Internet in ways that reduce the ability of businesses and entrepreneurs to use the Internet as a place for international commerce and limit the access of consumers to goods and services. This article discusses the importance of the Internet and cross‐border data flows for international trade. It proposes steps that governments should take to apply existing international trade rules and norms and identifies where new trade rules are required to further support the Internet and cross‐border data flows and drivers of international commerce and trade.

In this article, we will analyse contemporary carceral surveillance dynamics, namely the increasing storage and exchange of prisoners' biometric data. Drawing on qualitative research conducted in prisons, policing and security settings, we will explore how prisoners' bodies are reduced to information and broken up into data flows. These flows move within and beyond prison walls, impacting how prisoner's data is shared (in)formally with other criminal justice actors (e.g. police forces). Such interstitial connections allow us to better explore the permeability and porosity of prison boundaries. Overall, we argue that prisoners' data doubles are not spatially or physically restricted within cells and walls, as they circulate and are virtually managed at a distance. We urge to revisit and rethink the use of panoptic conceptual models when researching carceral spaces, its technological infrastructures and surveillance dynamics.

The democratization of computing and sensing through smart phones and embedded devices has led to widespread instrumentation of our personal and social spaces. The sensor data thus collected, has embedded in them minute details of our daily life. On the one hand, this has enabled a multitude of exciting applications where decisions at various time-scales are driven by inferences that are computationally derived from the shared sensory information and used for purposes such as targeted advertisements,behavior tailored interventions and automated control. On the other hand, the ability to derive rich inferences about user behaviors and contexts and their use in critical decision making also present various concerns of personal privacy. Prior approaches to handling the privacy concerns have often been ad hoc and focused on disassociating the user identity from the shared data, thus preventing an adversary from tracing a sensitive inference back to the user. However, in many application domains (e.g., mHealth, insurance) user identity is an inalienable part of the shared data. In such settings, instead of identity privacy, the focus is on the more general inference privacy problem, pertaining to the privacy of sensitive inferences that can be derived from the shared sensor data. The objective of this research has been to develop a principled understanding of the inference privacy problem and design formalisms, algorithms, and system mechanisms to effectively address it. The contributions of this dissertation are multi-fold. First, using information-theoretic notions we formulate the inference privacy problem in terms of a whitelist of utility providing allowed inferences, and a blacklist of sensitive inferences. We define utility and privacy parameters, derive bounds on the feasible region spanned by these parameters, and provide constructive schemes for achieving the boundary points of the feasible region. Second, using insights from the theoretical exploration, we design and implementipShield, a privacy-enforcing system by modifying the Android OS. ipShield, is a step towards reducing the user burden of configuring fine-grained privacy policies. It does so by changing the basic privacy abstraction, from access control on sensors to privacy preferences over higher level possible inferences. The user preferences are then used by a rule recommender to auto-generate privacy rules on sensors. Finally, we present iDeceit, a framework that implements model-based plausible falsification of sensor data to protect the privacy of sensitive inferences while maximizing the utility of the shared data. A graphical model is used to capture the temporal and spatial patterns that exists in user behavior. The model is then used, together with privacy and utility metrics and a novel plausibility metric, to generate falsified data stream that conforms to typical user-behavior ensuring perfect privacy. Extensive evaluation results are detailed for both ipShield and iDeceit to validate their efficiency and feasibility on mobile platforms.