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Important Dates

Paper Submission Deadline:

July 12, 2019
August 05, 2019 (AoE)

Camera-ready Deadline:
November 01, 2019

Conference Dates:
December 16 - 20, 2019

Publication Partners

Karthikeyan Bhargavan
Institut national de recherche en informatique et en automatique (INRIA)

Title: Secure Messaging: Towards Verified Standards and High Assurance Implementations

Modern messaging applications like WhatsApp and Skype rely on sophisticated cryptographic protocols to provide end-to-end security against powerful adversaries. These protocols are hard to get right, and harder still to implement correctly. Any logical flaw or cryptographic weakness in the design of a protocol, or any software bug in its implementation may lead to an attack that completely break its expected security guarantees. I advocate for the use of formal modeling and software verification to build verified messaging protocols with high assurance implementations. I will illustrate this proposed methodology using examples taken from the Signal protocol, which is used in a number of popular messengers, as well as new protocols proposed by the IETF Messaging Layer Security working group.

Brief Bio:
Karthikeyan Bhargavan (Karthik) is a directeur de recherche at Inria in Paris, where he leads a team of researchers working on developing new techniques for programming securely with cryptography. He was born in India and did his undergraduate studies at the Indian Institute of Technology Delhi before pursuing his PhD at the University of Pennsylvania. He then worked at Microsoft Research in Cambridge until 2009 when he moved to France. Karthik's research lies at the intersection of programming language design, formal verification, and applied cryptography. Most recently, his work has focused on the design and analysis of the TLS 1.3 Internet standard and the design and deployment of the HACL* cryptographic library.

Krishna P. Gummadi
Max Planck Institute for Software Systems (MPI-SWS)

Title: Privacy, Fairness, Transparency, and Abuse of Targeted Advertising on Social Media

All popular social media sites like Facebook, Twitter, and Pinterest are funded by advertising, and the detailed user data that these sites collect about their users make them attractive platforms for advertisers. In this talk, I will first present an overview of how social media sites enable advertisers to target their users. Next, I will pose and attempt to answer the following four high-level questions related to privacy, fairness, transparency, and abuse of social media advertising today.

  1. Privacy threats: what personal information about users are the sites leaking to advertisers to enable targeted ads?
  2. Fairness: can an advertiser target users in a discriminatory manner? If so, how can we detect and prevent discriminatory advertising?
  3. Transparency: can users learn what personal data about them is being used when they are targeted with an ad?
  4. Abuse: can malicious advertisers exploit personal data of users to increase societal discord?

Brief Bio:
Krishna Gummadi is a scientific director and head of the Networked Systems research group at the Max Planck Institute for Software Systems (MPI-SWS) in Germany. He also holds an honorary professorship at the University of Saarland. He received his Ph.D. (2005) and B.Tech. (2000) degrees in Computer Science and Engineering from the University of Washington and the Indian Institute of Technology, Madras, respectively.

Krishna's research interests are in the measurement, analysis, design, and evaluation of complex Internet-scale systems. His current projects focus on understanding and building social computing systems. Specifically, they tackle the challenges associated with (i) assessing the credibility of information shared by anonymous online crowds, (ii) understanding and controlling privacy risks for users sharing data on online forums, (iii) understanding, predicting and influencing human behaviors on social media sites (e.g., viral information diffusion), and (iv) enhancing fairness and transparency of machine (data-driven) decision making in social computing systems.

Krishna's work on online social networks, Internet access networks, and peer-to-peer systems has been widely cited and his papers have received numerous awards, including SIGCOMM Test of Time, Casper Bowden Privacy Enhancing Technologies (PET) Runners-Up Award, IW3C2 WWW Best Paper Honorable Mention, and Best Papers at NIPS ML & Law Symposium, ACM COSN, ACM/Usenix SOUPS, AAAI ICWSM, Usenix OSDI, ACM SIGCOMM IMC, ACM SIGCOMM CCR, and SPIE MMCN. He has also co-chaired AAAI's ICWSM 2016, IW3C2 WWW 2015, ACM COSN 2014, and ACM IMC 2013 conferences. He received an ERC Advanced Grant in 2017 to investigate "Foundations for Fair Social Computing".

Manoj Prabhakaran
Indian Institute of Technology Bombay (IITB)

Title: CellTree: A New Paradigm for Distributed Data Repositories

We present a new architecture for distributed data repositories. A design goal of the architecture is to let a CellTree evolve organically over time, and adapt itself to multiple applications. We provide provable guarantees of liveness, correctness and consistency, for a simple instantiation of the CellTree architecture. We also discuss several novel features of a CellTree that can be exploited by applications.

Brief Bio:
Manoj Prabhakaran is the Vijay and Sita Vashee chair Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (IIT) Bombay. His research interests span theoretical cryptography, information security and various topics in theoretical computer science and information theory.
Prior to joining IIT Bombay he was an Assistant/Associate Professor of Computer Science at the University of Illinois, Urbana-Champaign, from 2005 to 2016. He received a Ph.D. in Computer Science from Princeton University in 2005. Manoj graduated from IIT Bombay in 2000, with a B.Tech in Computer Science and Engineering and the Institute Gold Medal. He has received an IBM Ph.D. Fellowship, an NSF CAREER award, a Beckman Faculty Fellowship, and a Ramanujan Fellowship. He is an Associate Editor of the Journal of Cryptology and a member of the steering committees for the Theory of Cryptography Conference.

Reza Shokri
National University of Singapore (NUS)

Title: Trusting Machine Learning: Privacy, Robustness, and Interpretability Challenges

Machine learning algorithms have shown an unprecedented predictive power for many complex learning tasks. As they are increasingly being deployed in large scale critical applications for processing various types of data, new questions related to their trustworthiness would arise. Can machine learning algorithms be trusted to have access to individuals' sensitive data? Can they be robust against noisy or adversarially perturbed data? Can we reliably interpret their learning process, and explain their predictions? In this talk, I will go over the challenges of building trustworthy machine learning algorithms in centralized and distributed (federated) settings, and will discuss the inter-relation between privacy, robustness, and interpretability.

Brief Bio:
Reza shokri is an Assistant Professor of Computer Science at the National University of Singapore (NUS), where he holds the NUS Presidential Young Professorship. His research is on adversarial and privacy-preserving computation, notably for machine learning algorithms. He is an active member of the security and privacy community, and has served as a PC member of IEEE S&P, ACM CCS, Usenix Security, NDSS, and PETS. He received the Caspar Bowden Award for Outstanding Research in Privacy Enhancing Technologies in 2018, for his work on analyzing the privacy risks of machine learning models, and was a runner-up in 2012, for his work on quantifying location privacy. He obtained his PhD from EPFL.