posted by user: fmannhardt || 1063 views || tracked by 3 users: [display]

TPSIE 2019 : 1st Workshop on Trust and Privacy Aspects of Smart Information Environments

FacebookTwitterLinkedInGoogle

Link: https://www.tpsie.com
 
When Sep 18, 2019 - Sep 20, 2019
Where Trondheim
Submission Deadline Mar 31, 2019
Notification Due May 1, 2019
Final Version Due May 31, 2019
Categories    privacy   information systems   trust   computer science
 

Call For Papers

Background

Personalized support and services, based on data analytics, have been on the rise the last decade. The scale and dimensions of data gathering and shared in smart information environment (SIE) is sometimes hard for users (data subjects) to fathom and leaves them wondering when, why and how data was collected, or information generated. Much of this is due to the combined power of an abundance of data, data analytics methods and machine learning. Machine learning may, for example, be used to support (or automate) repetitive work, warnings of potential errors, and sense non-compliant behavior.

Despite the many advantages that such smart information environments offer, there are concerns about the responsible use of the data collected. Specifically, the new European regulations on data protection and privacy, GDPR, have raised awareness on privacy issues and causes concerns for designers and developers of smart information systems. In the age of information and digital technology, the focus of privacy has been on the protection of data directly or indirectly pertaining to a person; i.e. protection of personal information and reduction of risks for data subjects. Emphasis has been on data security and several methods, frameworks and techniques have been developed for ensuring appropriate data security. However, in the age of big data, machine learning, ubiquitous computing and social networks, such a data-centric view is inadequate and the need for a more user-centric view of privacy and protection of user data are required. In fact, with increasing availability of data, technology to aggregate and the possibility to conduct sophisticated analyses, the need to protect data and informational privacy is more important than ever before. This is also critical to build smart information environments that users can trust.

For a user-centric view, research has shown that there is a mismatch between legitimate concerns about privacy and actual behavior when it comes to sharing personal information (the "Privacy Paradox"). For those building systems it is necessary to navigate the (users) needs for personalization and wishes to remain anonymous ("Personalization Paradox"). It is indeed this paradox that SIE designers and organizations are faced with when designing services to support people in the various arenas in their personal or work lives and to enhance and foster knowledge sharing among people. Many appreciate the personalized recommendations on websites or personalized messages and notifications received through social media and other online services but oppose the invasion of their privacy. This in turn requires IT designers and developers of SIE to practice privacy-by-design or privacy-by-architecture within the design of SIE and calls for anonymization and cryptographical data protection techniques for log files.
Goals

The need to discuss issues related to privacy and trust in smart information environments is an important and highly relevant topic. This workshop's main objective is to start a dialogue and bring together a multi-disciplinary group of researchers, industry and practitioners to share their research, ideas, experiences and concerns in area of organizational and technology privacy and trust in smart information environments. The topics of interest for this workshop, but not limited to, are provided below.
Topics

Privacy and trust by design in SIE
Privacy and trust in SIE
System design for privacy awareness
Privacy and trust in (big) data analytics
Privacy-preserving data / process mining
Privacy engineering for (event) logs
Privacy and trust in machine learning
Privacy and trust in data aggregation
Privacy and trust in personalized services
Privacy and trust at the workplace
Privacy and trust and human factors
Privacy and trust in organizational data collection
Empirical analysis of GDPR compliance in service repositories
End-user privacy and trust control/management in SIE
Techniques for GDPR compliant modeling
Methods and techniques for privacy and trust management in SIE

Related Resources

SSCI 2019   The 2019 IEEE Symposium Series on Computational Intelligence
ICISSP 2020   6th International Conference on Information Systems Security and Privacy
ACM-MLNLP-Ei/Scopus 2019   2019 2nd International Conference on Machine Learning and Natural Language Processing
IoT-SE_Book 2019   Internet of Things and Secure Smart Environments: Success and Pitfalls
ISBDAI 2020   【Ei Compendex Scopus】2018 International Symposium on Big Data and Artificial Intelligence
WiCom-5G-SEC 2019   IEEE Wireless Communications Special Issue on Challenges and Novel Solutions for 5G Network Security, Privacy and Trust
ACM-ACAI-Ei/Scopus 2019   2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence
CTRQ 2020   The Thirteenth International Conference on Communication Theory, Reliability, and Quality of Service
ICAIML 2019   【EI SCOPUS CPCI】2019 1st International Conference on Artificial Intelligence and Machine Learning
IEEE TPS 2019   The First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications