December 2019 - talks - lists.cs.princeton.edu

Yixin Sun will present her FPO "Enhancing Anonymity Systems under Network and User Dynamics" on Tuesday, December 17, 2019 at 9:30am in CS 402.
by Nicki Mahler 09 Dec '19

09 Dec '19

Yixin Sun will present her FPO "Enhancing Anonymity Systems under Network and User Dynamics" on Tuesday, December 17, 2019 at 9:30am in CS 402. The members of her committee are as follows: Advisers: Mung Chiang and Prateek Mittal; Readers: Jennifer Rexford, Prateek Mittal, and Nick Feamster; Nonreaders: Jennifer Rexford, Ed Felten, and Mung Chiang. A copy of her thesis is available upon request. Please email ngotsis(a)cs.princeton if you would like a copy of the thesis. Everyone is invited to attend her talk. The talk title and abstract follow below. Privacy on the Internet is eroding rapidly. Anonymity systems have been designed to protect the identity and privacy of users from untrusted destinations and third parties on the Internet. However, many prior works on anonymity systems assume that important system components, such as network routing and user location, are static. In practice, these components are highly dynamic, leading to new vulnerabilities that can compromise user anonymity. In this dissertation, we exploit dynamics in anonymity systems across two different layers: network dynamics (i.e., changes in routing) at the network layer and user dynamics (i.e., changes in user location) at the application layer. First, we present RAPTOR attacks that exploit the dynamics in Internet routing to compromise user anonymity in the Tor network. The attacks enable adversaries to observe more user traffic and deanonymize them more effectively than previously thought. We successfully demonstrate the attacks by performing them on the live Tor network, ethically. Second, we build proactive and reactive defenses to protect Tor users from RAPTOR attacks: (1) a novel Tor relay selection algorithm that proactively reduces the probability of Tor users being affected by an attack, and (2) a monitoring system with novel detection analytics that detect routing anomaly for Tor relays in real time. Finally, we present attacks that exploit user mobility to compromise user anonymity across a wide range of anonymity systems. While prior works assume that user locations are fixed when accessing anonymity systems, we demonstrate that users can be highly mobile and subsequently expose themselves to adversaries as they visit more locations. In summary, we demonstrate the dangers of abstracting dynamics in Internet routing and user location from the analysis of anonymity systems, and take the step to design anonymity systems with these important system components in mind.

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Colloquium Speaker Subhash Khot TODAY at 12:30pm
by Emily C. Lawrence 09 Dec '19

09 Dec '19

Distinguished CS Colloquium Speaker Subhash Khot, New York University Monday, December 9 - 12:30pm Computer Science - Room 105 Host: Ran Raz [ https://www.cs.princeton.edu/events/25898 | https://www.cs.princeton.edu/events/25898 ] Hardness of Approximation: From the PCP Theorem to the 2-to-2 Games Theorem Computer scientists firmly believe that no algorithm can efficiently compute optimal solutions to a class of problems called NP-hard problems. For many NP-hard problems, even computing an approximately optimal solution remains NP-hard. This phenomenon, known as the hardness of approximation, has numerous connections to proof checking, optimization, combinatorics , discrete Fourier analysis, and geometry. The talk will provide an overview of these connections. It will address why graph coloring is a computationally hard problem, how it is possible to check a proof without even looking at it, why computer scientists love the majority vote, and whether a shape exists that looks spherical as well as cubical. It will explain how all this fits into a 30-year research program starting with the foundational Probabilistically Checkable Proofs (PCP) Theorem and leading to the recent 2-to-2 Games Theorem. Bio: Subhash Khot is a professor of computer science at the Courant Institute of Mathematical Sciences at New York University. His prior affiliations include Princeton University (PhD), Institute for Advanced Study (member), Georgia Tech (assistant professor) and University of Chicago (visiting professor). His research centers on computational complexity and its connections to geometry and analysis. He is a recipient of the National Science Foundation’s Alan T. Waterman Award, the International Mathematical Union’s Nevanlinna Prize, the Simons Investigator Award, a MacArthur Fellowship, and is a Fellow of the Royal Society.

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Reminder: Colloquium Speaker Subhash Khot, Monday Dec 9
by Emily C. Lawrence 06 Dec '19

06 Dec '19

Distinguished CS Colloquium Speaker Subhash Khot, New York University Monday, December 9 - 12:30pm Computer Science - Room 105 Host: Ran Raz [ https://www.cs.princeton.edu/events/25898 | https://www.cs.princeton.edu/events/25898 ] Hardness of Approximation: From the PCP Theorem to the 2-to-2 Games Theorem Computer scientists firmly believe that no algorithm can efficiently compute optimal solutions to a class of problems called NP-hard problems. For many NP-hard problems, even computing an approximately optimal solution remains NP-hard. This phenomenon, known as the hardness of approximation, has numerous connections to proof checking, optimization, combinatorics , discrete Fourier analysis, and geometry. The talk will provide an overview of these connections. It will address why graph coloring is a computationally hard problem, how it is possible to check a proof without even looking at it, why computer scientists love the majority vote, and whether a shape exists that looks spherical as well as cubical. It will explain how all this fits into a 30-year research program starting with the foundational Probabilistically Checkable Proofs (PCP) Theorem and leading to the recent 2-to-2 Games Theorem. Bio: Subhash Khot is a professor of computer science at the Courant Institute of Mathematical Sciences at New York University. His prior affiliations include Princeton University (PhD), Institute for Advanced Study (member), Georgia Tech (assistant professor) and University of Chicago (visiting professor). His research centers on computational complexity and its connections to geometry and analysis. He is a recipient of the National Science Foundation’s Alan T. Waterman Award, the International Mathematical Union’s Nevanlinna Prize, the Simons Investigator Award, a MacArthur Fellowship, and is a Fellow of the Royal Society.

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Colloquium Speaker Tom Kwiatkowski TODAY at 1:30pm
by Emily C. Lawrence 03 Dec '19

03 Dec '19

CS Colloquium Speaker Tom Kwiatkowski, Google Tuesday, December 3 - 1:30PM Computer Science - Room 104 Host: Karthik Narasimhan [ https://www.cs.princeton.edu/events/25905 | https://www.cs.princeton.edu/events/25905 ] New Challenges in Question Answering: Natural Questions and Going Beyond Word Matching Abstract: Recently, learned deep models have surpassed human performance on a number of question answering benchmarks such as SQuAD . However, these models resort to simple word matching and answer typing heuristics, and they are easily fooled. In this talk, I will present two different lines of work that aim to take us beyond the current status quo in question answering, and push us towards more robust representations of semantics, pragmatics, and knowledge. First, I will present Natural Questions (NQ), a new question answering benchmark from Google. NQ contains real user questions, which require an understanding of the questioner's intent. NQ also requires systems to read entire Wikipedia pages to decide whether they fully answer a question. This is much harder than finding an answer given the knowledge that one is present. I will convince you that the question 'when was the last time a hurricane hit Massachusetts?' is under-specified with many reasonable answers, and I will tell you how we developed robust evaluation metrics to deal with this ambiguity. In the second part of the talk I will present a complementary method of challenging today's question answering systems by removing access to evidence documents at inference time. Instead of building joint representations of questions and documents, we perform ahead of inference time reading and retrieve answers via fast maximum inner product search. I will show that this leads to large gains in accuracy and speed when finding answers in very large corpora. I will also show some preliminary results that show how our methods can be used to aggregate information from multiple diverse documents.

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Noah Apthorpe will present his Pre FPO "Network Privacy and User Protection in the Internet of Things" on Wednesday, December 4, 2019 at 10:30am in CS 402
by Nicki Mahler 02 Dec '19

02 Dec '19

Noah Apthorpe will present his Pre FPO, "Network Privacy and User Protection in the Internet of Things" on Wednesday, December 4, 2019 at 10:30am in CS 402. The members of his committee are as follows: Nick Feamster (adviser), Examiners: Ed Felten and Arvind Narayanan; Readers: Jennifer Rexford and Marshini Chetty Everyone is invited to attend his talk. The talk abstract follows below. In this talk, I will present my research combining networks, human-computer interaction, and machine learning to study privacy repercussions of Internet of things (IoT) devices and associated online services. This interdisciplinary approach has provided a unique perspective for measuring privacy issues at scale, protecting users from privacy vulnerabilities, and understanding how privacy norms interact with data collection and regulation. First, I will demonstrate that passive network observers can easily infer private in-home user activities from IoT network traffic metadata. This is possible because most user interactions with home IoT devices cause noticeable changes in network traffic rates that are detectable with thresholds or machine learning methods. The limited functionality of these devices makes it easy to associate these traffic rate changes with specific user activities. Fixed distribution traffic shaping can efficiently protect IoT devices that tolerate long network latencies or that have relatively constant traffic rates, but devices with more complicated network activity require more sophisticated protections. I will discuss a zero-latency traffic shaping algorithm, “Stochastic Traffic Padding,” that mimics user activity patterns, provides probabilistic limits on adversary inference accuracy, and allows for tuning of privacy protection versus bandwidth overhead. Next, I will present a survey instrument based on the theory of contextual integrity that allows researchers to measure individual and societal privacy norms at scale. My collaborators and I first applied this survey method to discover home IoT privacy norms of 1,731 U.S. adults, providing recommendations for IoT device manufacturers, regulators, and consumer advocates. We then surveyed 195 U.S. parents, showing that data handling requirements from the Children’s Online Privacy Protection Act (COPPA) generally align with parents’ privacy expectations for IoT toys. However, observed variations in the perceived acceptability of data collection across specific toys, information types, and other conditions emphasize the importance of detailed contextual factors to privacy norms and suggest potential improvements to COPPA. I will conclude with a summary of my ongoing research and a timeline for the completion of my degree.

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Reminder: Colloquium Speaker Tom Kwiatkowski, tomorrow at 1:30pm
by Emily C. Lawrence 02 Dec '19

02 Dec '19

CS Colloquium Speaker Tom Kwiatkowski, Google Tuesday, December 3 - 1:30PM Computer Science - Room 104 Host: Karthik Narasimhan [ https://www.cs.princeton.edu/events/25905 | https://www.cs.princeton.edu/events/25905 ] New Challenges in Question Answering: Natural Questions and Going Beyond Word Matching Abstract: Recently, learned deep models have surpassed human performance on a number of question answering benchmarks such as SQuAD . However, these models resort to simple word matching and answer typing heuristics, and they are easily fooled. In this talk, I will present two different lines of work that aim to take us beyond the current status quo in question answering, and push us towards more robust representations of semantics, pragmatics, and knowledge. First, I will present Natural Questions (NQ), a new question answering benchmark from Google. NQ contains real user questions, which require an understanding of the questioner's intent. NQ also requires systems to read entire Wikipedia pages to decide whether they fully answer a question. This is much harder than finding an answer given the knowledge that one is present. I will convince you that the question 'when was the last time a hurricane hit Massachusetts?' is under-specified with many reasonable answers, and I will tell you how we developed robust evaluation metrics to deal with this ambiguity. In the second part of the talk I will present a complementary method of challenging today's question answering systems by removing access to evidence documents at inference time. Instead of building joint representations of questions and documents, we perform ahead of inference time reading and retrieve answers via fast maximum inner product search. I will show that this leads to large gains in accuracy and speed when finding answers in very large corpora. I will also show some preliminary results that show how our methods can be used to aggregate information from multiple diverse documents.

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