This year’s program seeks to highlight challenges to privacy posed by widespread adoption of machine learning and artificial intelligence technologies. One motivation for this focus stems from goals and provisions of the European General Data Protection Regulation (GDPR), including requirements for privacy and data protection by design, providing notices and information about the logic of automated decision-making, and emphasis on privacy management and accountability structures in organizations that process personal data. Interpreting and operationalizing these requirements for systems that employ machine learning and artificial intelligence technologies is a daunting task.
As engineering is asked to play a larger role in privacy governance, software developers need tools for understanding, systematizing, and embedding privacy into systems and workflows. This work also requires greater engagement with design, legal, and public policy departments. Methods and tools for bridging privacy work across these communities are essential to success. Furthermore, research that focuses on techniques and tools that can aid the translation of legal and normative concepts into systems requirements are of great value.
Organizations also need tools for systematically evaluating whether systems fulfill users’ privacy needs and requirements and for providing necessary technical assurances. Methods that can support organizations and engineers in developing (socio-)technical systems that address these requirements is of increasing value to respond to the existing societal challenges associated with privacy.
Topics of interests include, but are not limited to:
Integrating law and policy compliance into the development process
Privacy or data protection impact assessments in the engineering context
Privacy engineering and data driven software development
Privacy engineering and machine learning
Privacy engineering and artificial intelligence
Privacy engineering and data subject access rights
Privacy risk management models
Privacy breach recovery methods
Privacy engineering and data portability
Technical standards, heuristics and best practices for privacy engineering
Privacy engineering in technical standards
Privacy requirements elicitation and analysis methods
User privacy and data protection requirements
Management of privacy requirements with other system requirements
Privacy requirements elicitation and analysis techniques
Privacy design patterns
Privacy-preserving architectures
Privacy engineering and databases, services and the cloud
Privacy engineering in networks
Engineering techniques for fairness, transparency, and privacy in databases
Privacy engineering in the context of interaction design and usability
Privacy testing and evaluation methods
Validation and verification of privacy requirements
Privacy Engineering and design
Engineering Privacy Enhancing Technologies (PETs)
Integration of PETs into systems or the development ecosystem
Models and approaches for the verification of privacy properties
Tools and formal languages supporting privacy engineering
Usable privacy for developers
Teaching and training privacy engineering
Adaptations of privacy engineering into specific software development processes
Pilots and real-world applications
Evaluation of privacy engineering methods, technologies and tools
Privacy engineering and accountability
Privacy engineering and business processes
Privacy engineering and manageability of data in (large) enterprises
Organizational, legal, political and economic aspects of privacy engineering
04月27日
2018
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