The International Workshop on Anonymization Methodologies and Privacy Modules for Internet-of-Things (AMPMIoT-2017) in conjunction with 10th IEEE International Conference on Cyber, Physical, and Social Computing (CPSCom-2017) 21 -23 June 2017;Exeter, United Kingdom.

Advancements made in sensing devices and internet connectivity has transformed wireless sensor networks from merely monitoring the environment to smart devices worn on wrist and deployed in home environment to sense, inform and respond to the changing environment, commonly referred as Internet of Things (IoT). Gartner estimated that total number of IoT devices will hit 26 billion units by 2020. To enable innovative use of data generated from these devices the UK Government Chief Scientific Adviser recommended to mandate all public bodies and regulated industries to make machine-readable data (IoT data streams) accessible through application programming interfaces. IoT has significantly evolved over the past couple of years from a simple connected fridge to smart and collaborative spaces. IoT devices and spaces generate data streams capturing data from different modalities (i.e., smartphones, wearable devices, ubiquitously deployed sensors) assisting service providers to provision contextually informed services.

Anonymization methodologies and privacy models have been rigorously researched to publish private and sensitive data whilst ensuring privacy of the data and involved stakeholders. So far, these methodologies and models have been developed to process data in an environment with limited number of data sources, formats in which data is generated, and service providers seeking access to the published data. IoT is fundamentally different than wireless sensor networks and service oriented computing paradigm. Factors like, uncertainly of data sources, trust on entities generating data streams, speed and variety in which data is accumulated, greatly affect conventional anonymization methodologies and privacy models. Advancements made in data stream processing and integration, machine learning, data modeling, service provisioning models and new policy initiatives to utilise the wealth of data generated through IoT can be well utilised to excel research and development in anonymization and privacy – whilst considering privacy of involved stakeholders, usability of the published data, and ethical issues.

This aim of Anonymization Methodologies and Privacy Modules for Internet-of-Things (AMPMIoT-2017) is to understand data publishing and privacy requirements of IoT and report new developments, particularly for application areas of healthcare, smart cities, data driven services and networks, and beyond. This workshop will bring researchers from data security, privacy, trust, stream processing & management, data modeling, machine learning, computer networks, big data, and complementary areas. During the workshop data privacy and usability aspects will be discussed within the context of IoT devices, services & frameworks, and access to published data.
 
List of Topics:
• Topics of interest include, but not limited to the following:
• Data stream anonymization
• IoT privacy models
• Privacy-aware IoT data integration
• Secure data stream processing
• Privacy-aware data capturing
• Data stream usability measures – information loss, imputation and alike.
• Data stream security and privacy protocols
• Privacy-aware data modeling and representation
• Data stream clustering and classification
• Data stream feature selection
• Machine learning for data stream processing
• IoT service provisioning models


All accepted papers will be included in the The 2017 IEEE International Conference on Cyber, Physical, and Social Computing (CPSCom-2017) proceedings published by IEEE Computer Society Press and indexed in IEEE Xplore Digital Library.

Workshop General Chairs:
Dr. Zeeshan Pervez – University of the West of Scotland, United Kingdom
Prof Keshav Dahal – University of the West of Scotland, United Kingdom
Programme Committee:
Dr. Zaheer Khan – University of the West of England, United Kingdom
Dr. Adil Mahmood Khan – Innopolis University, Russia
Dr. Patrick Hung – University of Ontario Institute of Technology, Canada
Dr. Asad Masood Khattak – Zayed University, United Arab Emirates
Dr. Donghai Guan – Nanjing University of Aeronautics and Astronautics, China
Dr. Muhammad Fahim – Istanbul Sabahhatin Zaim University, Turkey
Dr. Adín Ramírez Rivera - University of Campinas, Brazil
Dr. Wajahat Ali Khan – Kyung Hee University, South Korea