The First International Workshop on Machine Learning Security and Privacy: Experiences and Applications

Call for Papers:

In recent years Artificial Intelligence(AI) and Machine Learning(ML), especially deep learning, have demonstrated their superior performance on a wide variety of complex tasks including speech recognition, natural language processing, image classification, game playing and autonomous vehicles. These successes have stimulated a surge of interests in applying AI and ML techniques into communication systems and networks to deal with problems such as radio access technology classification, low energy consumption in wireless sensor networks, and the management of large scale Internet of Things (IoT). Although ML offer a new and promising design regime to wireless network systems, it has been shown that ML models and systems could severely suffers from various adversarial attacks and privacy risks. The impact of these ML-based security and privacy attacks on the wireless systems are not yet well understood and little research work has been done on it.

On the other hand, the world is moving to digitalization and intelligentization for the long term. We are at an important point in this evolution, as new forces emerge and combine to create new ways for cities to work. For instance, insights from information transfer across platforms can be exploited to reduce accidents, improve air quality, and alert disaster events. Cyber-physical systems (CPS) also bring new risks that arise due to the unexpected interaction within city services. These safety risks arise because of information that distracts users while driving, software errors in medical devices, corner cases in data-driven control, compromised sensors in drones or conflicts in societal policies. In parallel, artificial intelligence flourishes the development of cities, revolutionizing the way that public services are interacted with citizens. The data that drives the smarter city must be secure, to safely fuel unhindered progress.

Therefore, this workshop aims to bring together experts from machine learning, security, privacy, wireless communication communities and smart city to share the latest research findings, exchange ideas, experiences and work-in-process related to all aspects of secure and private machine learning applied to communication and networking systems. Finally, we hope to chart out important research directions for future work and foster research collaborations.

Topics of Interest (but not limited to):

    • AI/ML security
    • Adversarial attacks on machine learning models and algorithms
    • Defenses against adversarial attacks
    • Data poisoning attacks on machine learning
    • Trojan/backdoor attacks on machine learning
    • Security of deep learning systems
    • Model stealing
  • Privacy of Machine Learning
    • Membership inference attacks
    • Model Inversion attacks
    • Machine learning with differentially privacy
  • Smart City Security and Privacy
    • Security and privacy of smart city networking, services and infrastructures and reliability
    • Security and privacy of smart utilities, smart grid, consumption, sensing, and Internet of Things
    • Security and privacy of smart city big data, open data, and urban computing
    • Modeling security, safety, and privacy for smart cities
    • Security and privacy of smart transportation system planning, evaluation, and technologies
    • Assured smart city sewage, water and electricity management
    • Smart city privacy-aware healthcare service and medical CPS
    • Smart city crime watching and alerting systems
    • Security and privacy of smart homes, smart building, and social community networks infrastructures
  • ML applications in wireless communication systems
    • RF fingerprinting
    • Localization
    • Smart jamming
    • Intrusion and malware Detection
    • Usable security and privacy for IoT

Submission Guidelines:

Papers must be submitted via EDAS in the following link: https://easychair.org/conferences/?conf=wisecml19

Submitted papers should be written in the English language, with a maximum page limit of 6 printed pages, including all the figures, references and appendices, and not published or under review elsewhere. Papers longer than 6 pages will not be reviewed. Use the standard IEEE Conference templates for Microsoft Word or LaTeX formats found at: https://www.ieee.org/conferences/publishing/templates.html.

Regardless of the source of your paper formatting, you must submit your paper in the Adobe PDF format. The paper must print clearly and legibly, including all the figures, on standard black-and-white printers. Reviewers are not required to read your paper in color.

If the paper is typeset in LaTeX:

  • Please use an unmodified version of the LaTeX template IEEEtran.cls version 1.8, and use the preamble: \documentclass[10pt, conference, letterpaper]{IEEEtran}.
  • Do not use additional LaTeX commands or packages to override and change the default typesetting choices in the template, including line spacing, font sizes, margins, space between the columns, and font types. This implies that the manuscript must use 10 point Times font, two-column formatting, as well as all default margins and line spacing requirements as dictated by the original version of IEEEtran.cls version 1.8.

If you are using Microsoft Word to format your paper:

  • You should use an unmodified version of the Microsoft Word IEEE Transactions template (US letter size).

More information and template downloads can be found at the IEEE MASS main page.

Timeline:

  • Paper Submission Deadline: September 16, 2019 (Anywhere on Earth)
  • Notification of Acceptance: October 4, 2019
  • Camera-ready version: October 15, 2019 (Firm deadline)