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This unique 3-day training summit focuses on the intersection of technology and domestic violence, sexual assault, stalking, and trafficking. Covering a wide range of technology-related issues, this conference will be helpful to advocates, law enforcement, and legal professionals who work with survivors of abuse.

With the continuous proliferation of diverse Internet-based computing paradigms, large amounts of data containing privacy-sensitive information are being constantly published, collected, processed, and archived. This trend will be further fueled up by the new development of IoT (Internet of Things) technologies, smart cities, e-health, e-commerce, social and behavioral studies, social networking, edge computing, and cloud computing. As a result, fast-growing concerns about data privacy from academia as well as industry emerge in recent years, which motivate researchers and practitioners to think about questions such as how to guarantee that the collected or published data are not misused; how to ensure that data processing does not disclose any sensitive information; how to store the data securely for privacy protection; how to define new privacy policies that allow desirable services; and how to make sure that privacy policies issued by government and industry are not violated.

The IEEE Symposium on Privacy-Aware Computing (IEEE PAC) brings together experts from academia, government, and industry to present and discuss recent advances and new perspectives on related research and development in privacy-aware computing. We invite original theoretical contributions as well as system implementation/experimentation works on all topics related to “making computing privacy-aware” for privacy protection. Particularly, IEEE PAC solicits unpublished results in privacy threats and vulnerabilities of emerging applications for various computing platforms (mobile, IoT, cloud, social network, etc.), privacy-aware algorithms for big data analytics and networking, novel methodologies for privacy-protection (modern cryptography, game theory, etc.), policies for privacy-aware computing, etc.

Important Dates

• Submission Deadline: 04/22/2017(extended)
• Notification: 06/09/2017
• Camera-Ready Submission Date: 07/01/2017

From August 7-11, 2017, secUnity invites you to its second summer school at Technische Universität Darmstadt. This event is organized by the Chair for Software Business and Information Management (http://www.is.tu-darmstadt.de) and will focus on research topics related to economic and interdisciplinary approaches to IT security and privacy.

The secUnity International Summer School on Economics of IT Security and Privacy provides an opportunity to advanced Bachelor, Master and PhD students as well as young scholars to learn more about IT security and privacy in general as well as in relation to business models and legal aspects. The event will be a mix of lectures and hands-on exercises, allowing the students to learn how to design a business model in the context of IT security and privacy which will be reviewed at the end of the event. Furthermore, we offer a multifaceted program covering theoretical and practical aspects in the lectures.

Topics covered during the summer school include (among others):
• Introduction to IT Security and Privacy
• IT Security Management
• Ethical Hacking and National Security
• IT Security Research
• Entrepreneurship and E-Business
• Legal Aspects of IT Security and Privacy

The registration fee for the summer school is free for students and 500 Euro for industry participants. This fee includes daily refreshments, lunch, and social events.

*** scholarships for accommodation and travel for international participants available ***

This workshop aims to bring together a growing community of researchers, practitioners, and policymakers concerned with fairness, accountability, and transparency in machine learning. The past few years have seen growing recognition that machine learning raises novel ethical, policy, and legal challenges. In particular, policymakers, regulators, and advocates have expressed fears about the potentially discriminatory impact of machine learning and data-driven systems, with many calling for further technical research into the dangers of inadvertently encoding bias into automated decisions. At the same time, there is increasing alarm that the complexity of machine learning and opaqueness of data mining processes may reduce the justification for consequential decisions to “the algorithm made me do it” or “this is what the model says.” The goal of this workshop is to provide researchers with a venue to explore how to characterize and address these issues with computationally rigorous methods. We seek contributions that attempt to measure and mitigate bias in machine learning, to audit and evaluate machine learning models, and to render such models more interpretable and their decisions more explainable.

This year, the workshop is co-located with the 23rd SIGKDD conference on Knowledge Discovery and Data Mining (KDD 2017). The workshop will consist of a mix of invited talks, invited panels, and contributed talks. We welcome paper submissions that address any issue of fairness, accountability, and transparency related to machine learning. This year, we place a special emphasis on papers describing how to bring tools for ensuring fairness, accountability, or transparency into real world applications of machine learning. We especially welcome submissions from practitioners in industry, government, and civil society.