N2Women workshop aims to foster connections among the underrepresented women in communications, computer networking, and related research fields. The workshop also welcomes men who share the same research interests, often face similar career hurdles as women, acknowledge underrepresentation of women in the field and believe proactive action is required for improving retention rate.
Posters are solicited for research related to any aspect of networking and communications. All researchers in the networking and communications fields are welcome to submit their work for presentation at this workshop.
Posters will NOT be published in the ACM BuildSys/SenSys proceedings and can therefore be work in progress, under submission in other conferences or workshops, or a combination of recently published work.
Authors of accepted posters will be invited to present their work as part of a lightning presentations session, followed by an hour of interactive poster presentation. One poster will be selected for Best Poster Award.
The posters will be judged based on a) technical merit of the poster, b) presentation quality during the lightning talk as well as the poster presentation session. Presenting a poster is a great opportunity to receive interesting and valuable feedback on ongoing research from mentors and a knowledgeable crowd at the workshop.
Limited number of travel grants are available for students (undergraduate or graduate), postdocs or young professionals who present a poster at this workshop. Please check the Travel Grant web site for the grant policy and use the form for N2Women grant application. While filling in your application, please mention if you have a paper submitted to N2Women.
Please also see Travel Grant Application Deadline here.
Poster Submission GuidelinesEach submission should be formatted as an extended abstract, describing the research to be presented in the poster. All submissions should be written in English with a maximum paper length of TWO printed pages including all figures and references. Authors must follow the standard ACM two-column conference format (single-spaced 8.5” x 11” pages with 9-pt font size). The extended abstract must include the names, affiliations and email addresses of all authors and should be submitted as a single PDF file. All submissions must use the following LaTeX (preferred) styles.
You can use the following template: template download link
Please note that only PDF files will be accepted, and all submissions must be done through HotCRP.
Submission link: https://n2women19.hotcrp.com
- Paper Submission:
August 7, 2019 (4PM AoE)
- Acceptance Notification:
August 22nd, 2019
- Workshop Date:
November 10, 2019
For more information, please contact the Workshop Chairs:
- Ziqian (Cecilia) Dong (New York Institute of Technology, USA)
- Jorge Ortiz (Rutgers University, USA)
- Suzan Bayhan (TU Berlin, Germany and University of Twente, Netherlands)
- Catherine H Crawford (IBM Research)
- Eirini Eleni Tsiropoulou (University of New Mexico)
- Ella Peltonen (University of Oulu)
- Irene Manotas (IBM Research)
- Jay Taneja (University of Massachusetts Amherst)
- Ozlem Durmaz Incel (Galatasaray University)
- Setareh Maghsudi (TU Berlin)
Keynote 1: Three Transformations in the Globally Spreading Practice of Online CensorshipRoya Ensafi
University of Michigan
Abstract: Online censorship is spreading across the world, and changing form. Three transformations are underway in the global practice of online information control: commoditization of filtering technologies, the decentralization of filtering from government-run technical chokepoints to legally mandatory distributed enforcement by private ISPs, and political normalization and transnational spread of new filtering practices — often in the name of “data sovereignty” or national security. In this talk I will present three new research projects: (1) our latest framework for discovering and monitoring DPI-based filtering at Internet scale, (2) our in-depth multifaceted exploration of the mechanisms by which Russian government is developing more sophisticated controls over its decentralized networks, (3) our rapid investigation of, and response to, the Kazakh government’s attempted man-in-the-middle(MitM) attack against HTTPS connections — an investigation that led to Mozilla, Google, and Safari to take countermeasures to protect their Kazakh users. I argue that scalable measurement techniques, interdisciplinary and holistic investigation, and credible data-driven reports can arm technologists and policy communities to combat these threats.
Bio: Roya Ensafi is an Assistant Professor in Computer Science and Engineering at the University of Michigan, where her research focuses on computer networking, security, and privacy. She designs scalable techniques and systems to protect users’ Internet connections from disruption and surveillance. Roya is best known for her work in the area of Internet censorship, where she pioneered the use of side-channels to remotely measure adversarial manipulation of Internet traffic. She is a founder of CensoredPlanet a global censorship observatory that continuously monitors various types of network interference in over 170 countries since August 2018. Her notable projects with real-world impact include researching and documenting the Kazakhstan HTTPS MitM interception, the Great Cannon of China, and large-scale study of server-side geoblocking. She has received the NSF CISE Research Initiation Initiative award and the Google Faculty Research Award. Roya’s work has appeared in the popular press publications such as the NY Times, Wired, Business Insider, and ArsTechnica. Prior to joining Michigan, she was a postdoc at Princeton University’s Center for Information Technology Policy (CITP).
Keynote 2: Security and Privacy Guarantees in Machine Learning with Differential PrivacyRoxana Geambasu
Abstract: Machine learning (ML) is driving many of our applications and life-changing decisions. Yet, it is often brittle and unstable, making decisions that are hard to understand or can be exploited. Tiny changes to an input can cause dramatic changes in predictions; this results in decisions that surprise, appear unfair, or enable attack vectors such as adversarial examples. Moreover, models trained on users' data can encode not only general trends from large datasets but also very specific, personal information from these datasets; this threatens to expose users' secrets through ML models or predictions. This talk positions differential privacy (DP) -- a rigorous privacy theory -- as a versatile foundation for building into ML much-needed guarantees of security, stability, and privacy. I first present PixelDP (S&P'19), a scalable certified defense against adversarial example attacks that leverages DP theory to guarantee a level of robustness against these attacks. I then present Sage (SOSP'19), a DP ML platform that bounds the cumulative leakage of secrets through models while addressing some of the most pressing challenges of DP, such as running out of privacy budget and the privacy-accuracy tradeoff. PixelDP and Sage are designed from a pragmatic, systems perspective and illustrate that DP theory is powerful but requires adaptation to achieve practical guarantees for ML workloads.
Bio: Roxana Geambasu is an Associate Professor of Computer Science at Columbia University and a member of Columbia's Data Sciences Institute. She joined Columbia in Fall 2011 after finishing her Ph.D. at the University of Washington. For her work in cloud and mobile data privacy, she received: an Alfred P. Sloan Faculty Fellowship, an NSF CAREER award, a Microsoft Research Faculty Fellowship, several Google Faculty awards, a "Brilliant 10" Popular Science nomination, the Honorable Mention for the 2013 inaugural Dennis M. Ritchie Doctoral Dissertation Award, a William Chan Dissertation Award, two best paper awards at top systems conferences, and the first Google Ph.D. Fellowship in Cloud Computing.
- Hack me-do. Deploying an IoT Malware Laboratory to Analyze Malicious Behavior
- Accelerating Data-Driven Agriculture Using Wireless Soil Moisture Sensors
- Computer Vision for Graphology
- 3D Motion Characterization Using Stereo Camera for Vehicular Communications
- Predicting Job Completion Time in Vehicular Clouds
- DeepMAC: A Machine Learning-Based Automated Design Framework for MAC protocols
- Performance of IEEE 802.11ah Networks under Rician Fading Channels
- QoS in Software Defined IoT network using Block-chain based Smart Contract
- Predicting the aircraft engine’s health condition using classification algorithms and demonstrating the model performance measures
- Contribution to the Analysis of the Lifetimes of Well Functioning of Wireless Sensor Networks Application on 5G Infrastructure
- Resilient close neighbors collaborative service performance monitoring
- A Method for Studying Communications Resiliency in Emergency Plans
- Poster: A Secure and Smart Framework for Preventing Ransomware Attack
- Classification of MIMO Wireless Signals
- Poster Abstract: Deep Room Recognition Using Inaudible Echos
- Comparison of Throughput Analytical Models for IEEE 802.11ah Wireless Networks
- Performance Analysis for Cache-Enabled User-Centric Network