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Andrew Crabtree - Privacy by Design for the Internet of Things: Building accountability and security

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Andrew Crabtree Privacy by Design for the Internet of Things: Building accountability and security
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Privacy by design is a proactive approach that promotes privacy and data protection compliance throughout project lifecycles when storing or accessing personal data. Privacy by design is essential for the Internet of Things (IoT) as privacy concerns and accountability are being raised in an increasingly connected world. What becomes of data generated, collected or processed by the IoT is clearly an important question for all involved in the development, manufacturing, applications and use of related technologies. But this IoT concept does not work well with the big data trend of aggregating pools of data for new applications. Developers need to address privacy and security issues and legislative requirements at the design stage, and not as an afterthought.

In this edited book, the authors draw on a wealth of interdisciplinary research to delineate the challenges of building accountability into the Internet of Things and solutions for delivering on this critical societal challenge. This advanced book brings together legal-tech scholars, computer scientists, human computer interaction researchers and designers and socials scientists to address these challenges and elaborate solutions. It articulates the accountability principle in law and how it impacts IoT development, presents empirical studies of accountability in action and its implications for IoT development, brings technological responses to the requirements of GDPR and ways of building accountability into the IoT, and covers compliant IoT application development, privacy-preserving data analytics, human-centred IoT security, human-data interaction, and the methodological challenge of understanding and responding to the adoption of future technologies in everyday life.

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IET SECURITY SERIES 14 Privacy by Design for the Internet of Things Other - photo 1
IET SECURITY SERIES 14
Privacy by Design for the Internet of Things

Other volumes in this series:

Volume 1Information Security: Foundations, technologies and applications A. Awad, and M. Fairhurst (Editors)
Volume 2Engineering Secure Internet of Things Systems B. Aziz, A. Arenas and B. Crisp
Volume 3Mobile Biometrics G.Guo and H. Wechsler (Editors)
Volume 4User-Centric Privacy and Security in Biometrics C. Viuelhauer (Editor)
Volume 5Iris and Periocular Biometrics C. Rathgeb and C. Busch (Editors)
Volume 7Data Security in Cloud Computing V. Kumar, R. Ko and S. Chaisiri (Editors)
Volume 8Hand-Based Biometrics: Methods and Technology M. Drahansk (Editor)
Volume 9Authentication Technologies for Cloud Computing, IoT and Big Data Y.M. Alginah and M.N. Kabir (Editors)
Volume 10Nature-Inspired Cyber Security and Resiliency: Fundamentals, Techniques and Applications E. M. El-Alfy, M. Eltoweissy, E. W. Fulp, W. Mazurczyk (Editors)
Volume 12Voice Biometrics: Technology, trust and security C. Garca and G. Chollet (Editors)
Privacy by Design for the Internet of Things

Building accountability and security

Edited by
Andrew Crabtree, Hamed Haddadi and Richard Mortier

The Institution of Engineering and Technology

Published by The Institution of Engineering and Technology, London, United Kingdom

The Institution of Engineering and Technology is registered as a Charity in England & Wales (no. 211014) and Scotland (no. SC038698).

The Institution of Engineering and Technology 2021

First published 2021

This publication is copyright under the Berne Convention and the Universal Copyright Convention. All rights reserved. Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may be reproduced, stored or transmitted, in any form or by any means, only with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publisher at the undermentioned address:

The Institution of Engineering and Technology
Michael Faraday House
Six Hills Way, Stevenage
Herts, SG1 2AY, United Kingdom

www.theiet.org

While the authors and publisher believe that the information and guidance given in this work are correct, all parties must rely upon their own skill and judgement when making use of them. Neither the author nor publisher assumes any liability to anyone for any loss or damage caused by any error or omission in the work, whether such an error or omission is the result of negligence or any other cause. Any and all such liability is disclaimed.

The moral rights of the author to be identified as author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.

British Library Cataloguing in Publication Data
A catalogue record for this product is available from the British Library

ISBN 978-1-83953-139-2 (hardback)
ISBN 978-1-83953-140-8 (PDF)

Typeset in India by Exeter Premedia Services Private Limited
Printed in the UK by CPI Group (UK) Ltd, Croydon

Contents
List of figures
Putting citizens within the processing loop
Fundamental challenges for HDI
Associated challenges for HDI
The databox proxy (see [] for all cards)
Overall responses to deck one data cards (see [] for all responses)
An example IoT application, whereby sensor readings work to trigger an actuation in a smart home, with some data feeding to a city councils planning service. Even a simple scenario involves data flowing across a range of technical and organisational boundaries, as each arrow indicates. Decision provenance works to capture information about these data flows, thereby supporting review.
The IoT Databox model
The IoT Databox dashboard
The IoT Databox IDE
App manifest
Risk rating apps during development
The app store: displaying at-a-glance risk ratings
IDE node types
Personal data flows
Applying the schema (a)
Applying the schema (b)
Health insurance app quote
IDE DPIA recommendation
Googles federated learning framework []
Hybrid deep learning frameworks []
Accuracy of the personal model in the face of dumb and not-so-dumb adversaries. (a) Accuracy of the personal model for different percentages of corrupted samples in the training set of the shared model (dumb adversary). (b) Accuracy of the personal model for different percentages of corrupted samples in the training set of shared model (smarter adversary)
SGD (stochastic gradient descendent) using five different barrier control strategies. Probabilistic synchronous parallel achieves a good trade-off between efficiency and accuracy. (a) Progress distribution in steps. (b) CDF of nodes as a function of progress. No node maintains global state. (c) pBSP parameterised by different sample sizes from 0 to 64. Increasing the sample size decreases spread, shifting the curves to the left replicating behaviour from the most lenient (ASP) to the most strict (BSP)
Tokens, pot and reader
App device introduction
Association of tokens with devices
Association of pots with actions
Adoption hype cycle
Futures cone
Plurality futures diagram
Emerging technology futures
Design fiction worlds (scales and entry points)
Drone landing station and signage
Map of drone landing stations and enforcement zones
Allspark battery modules
Alternative home wiring
Allspark promotional material
Allspark feedback
Relationship of object types for living room of the future
Living Room of the Future installation
Break Up explainer video
List of tables
Accountability requirements in GDPR (from Urquhart et al. [])
Data controller(s) responsible to demonstrate accountability
Personal data types
Personal data attributes
Secondary data attributes
Accelerometer personal data schema
Classification of the synchronisation methods used by different systems
About the editors

Andrew Crabtree is a professor in the School of Computer Science at the University of Nottingham, UK. A sociologist by background, he started working with computer scientists and software engineers when he did his PhD with John Hughes at Lancaster University, UK. He has championed the use of ethnography in systems design and has been working on shaping computing around the social world for the last 25 years. He has written three textbooks on ethnography for design. In 2014, he became the first ethnographer to be awarded a senior EPSRC Fellowship to respond to the privacy and accountability challenge created by the IoT and the technological transformation of the home into a key site of personal data production.

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