- talks - lists.cs.princeton.edu

Ben Burgess General Exam Presentation Friday, May 18, 2018 at 10:00 am - CS302
by Barbara A. Mooring 16 May '18

16 May '18

Ben Burgess will present his General Exam Presentation Friday, May 18, 2018 at 10:00 am - CS302 Title: Verifiable Random Beacons Abstract: Random beacons sources of open, public randomness, and play a critical role in many protocols. Their uses range from proofs of earliest possible creation date to secure multiparty computation to lottery systems. Existing sources of public randomness, including various random beacons, are subject to single points of failure, such that an opportunistic adversary might be able to meaningfully affect the outcome. We develop a random beacon that always requires compromising more than one party to meaningfully affect the outcome. In order to further discourage dishonest behavior, every critical step of our protocol is third-party auditable and backed by economic incentives that make honest behavior more profitable than dishonest behavior. Our method uses a variant of a verifiable random function (VRF) to "scramble" a public input in an unpredictable, thresholded manner, making it difficult for anyone one party to predict how changes to the public input affect the output, or to affect or suppress the output. The proofs of the VRFs are verified by smart contracts to provide monetary reward or penalty, as appropriate, to protocol participants, ensuring honesty is the best fiscal policy. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Jude Nelson FPO on Tuesday, May 22 at 2:00 pm in CS402
by Barbara A. Mooring 15 May '18

15 May '18

Jude Nelson will present his FPO on Tuesday, May 22 at 2:00 pm in CS 402. Committee: Larry Peterson, Adviser Princeton University Kai Li, Princeton University Brian Kernighan, Princeton University John H. Hartman, University of Arizona All are welcomed to attend. Title: Wide-area Software-defined Storage Abstract: The proliferation of commodity cloud services helps developers build wide-area “system-of-systems” applications by harnessing cloud storage, CDNs, and public datasets as reusable building blocks. But to do so, developers must contend with two long-term challenges. First, whenever developers change storage providers, they must work to preserve the application’s expected storage semantics, i.e. the rules governing how the application expects the storage provider to handle its reads and writes. Today, changing storage providers is costly, because developers need to patch the application to make it compatible with the new provider’s data consistency model, access controls, replica placement strategies, and so on. At the same time, users have certain expectations about how their data will be used, which the application must meet. For example, depending on the application, users may expect that their data will be kept private from other users, that their data will be exportable to other applications, that accesses to their data will be logged in an auditable way, and so on. In the limit, each user’s expectations represent an implicit policy constraining how their data can be stored. Honoring these policies is difficult for developers who rely on third-party storage providers because the storage provider is often unaware of them. This thesis addresses these challenges with a wide-area storage protocol, called “software-defined storage” (SDS), that runs in-between applications and cloud ser- vices. SDS-enabled applications do not host data, but instead let users bring their preferred cloud services to the application. By taking a user-centric approach to hosting data, users are empowered to programmatically specify their policies inde- pendent of their applications and select services that will honor them. To support this approach and to tolerate service provider changes, SDS empowers developers to programmatically specify their application’s storage semantics independent of storage providers. This thesis presents the design principles for SDS, and validates their real-world applicability with two SDS implementations and several non-trivial applications built on top of them. Most of these applications are used in production today. This thesis presents early performance results of its SDS implementations and uses real-world experiences to show how to make the most of SDS. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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REMINDER - Arunesh Mathur General Exam Presentation TODAY, May 15, 2018 at 2:00 pm, CS402
by Barbara A. Mooring 15 May '18

15 May '18

REMINDER - Arunesh Mathur General Exam Presentation TODAY, May 15, 2018 at 2:00 pm, CS402 Title: Examining Advertising Dark Patterns on Social Media: An Empirical Study of Affiliate Marketing Endorsements Abstract: Dark patterns are persuasive techniques in interface design that nudge users towards making sub-optimal decisions in order to benefit the creator of the interface. A common dark pattern that manifests in online platforms involves disguising advertisements as testimonials, endorsements, or reviews to drive sales and clicks. Because users are often ill-equipped to identify such advertisements, the Federal Trade Commission (FTC) in the United States requires that these advertisements be adequately disclosed to avoid user deception and harm. In this study, we examined disclosures accompanying affiliate marketing, a type of endorsement marketing used by content creators on social media platforms. Specifically, we asked how many content creators disclose their affiliate marketing content to users, whether content creators follow the FTC’s disclosure guidelines in their disclosures, and whether users comprehend the disclosure statements. By examining over half a million YouTube videos and two million Pinterest pins, we found that only roughly one-tenth of affiliate marketing content on both platforms contains any form of disclosures. In the following user study, we discovered that users fail to understand shorter, non-explanatory disclosures. Based on our findings, we make various design and policy suggestions that can improve disclosure practices. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Andrew Or General Exam Presentation Wednesday, May 16, 2018 at 10:00 am - CS302
by Barbara A. Mooring 14 May '18

14 May '18

Andrew Or General Exam Presentation Wednesday, May 16, 2018 at 10:00 am - CS302 Title: Quality-aware cluster scheduling Abstract: Modern data analytics is increasingly concerned with quick response times. With the recent growth of data far exceeding the rate at which hardware has advanced, many applications have begun to explore options that return intermediate results early. For example, approximate query processing systems begin returning inexact answers before having processed the entire input data, and machine learning practitioners often stop the training process early once they are sufficiently satisfied with the training error. However, existing cluster schedulers are not well equipped for these kinds of applications. In particular, state-of-the-art scheduling solutions are primarily focused on resource fairness when making decisions, treating the applications running on the cluster as black boxes. This approach, though widely popular, often wastes a significant amount of resources on applications that have been running for a long time and are no longer making much progress due to diminishing marginal utility. This talk presents an alternative scheduling approach that makes decisions based on application utility. The intuition behind this approach is that prioritizing fresh applications can lead to cluster-wide utility improvement. We have implemented such a scheduler in Spark and will discuss its effectiveness in the context of approximate query processing and machine learning. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Arnesh Mathur General Exam Presentation Tuesday, May 15, 2018 at 2:00 pm - CS402
by Barbara A. Mooring 14 May '18

14 May '18

Arnesh Mathur General Exam Presentation Tuesday, May 15, 2018 at 2:00 pm - CS402 Title: Examining Advertising Dark Patterns on Social Media: An Empirical Study of Affiliate Marketing Endorsements Abstract: Dark patterns are persuasive techniques in interface design that nudge users towards making sub-optimal decisions in order to benefit the creator of the interface. A common dark pattern that manifests in online platforms involves disguising advertisements as testimonials, endorsements, or reviews to drive sales and clicks. Because users are often ill-equipped to identify such advertisements, the Federal Trade Commission (FTC) in the United States requires that these advertisements be adequately disclosed to avoid user deception and harm. In this study, we examined disclosures accompanying affiliate marketing, a type of endorsement marketing used by content creators on social media platforms. Specifically, we asked how many content creators disclose their affiliate marketing content to users, whether content creators follow the FTC’s disclosure guidelines in their disclosures, and whether users comprehend the disclosure statements. By examining over half a million YouTube videos and two million Pinterest pins, we found that only roughly one-tenth of affiliate marketing content on both platforms contains any form of disclosures. In the following user study, we discovered that users fail to understand shorter, non-explanatory disclosures. Based on our findings, we make various design and policy suggestions that can improve disclosure practices. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Malte Moeser General Exam Presentation - May 16, 2018 at 5:00pm - CS402
by Barbara A. Mooring 14 May '18

14 May '18

Malte Moeser General Exam Presentation - May 16, 2018 at 5:00pm - CS402 Title: Anonymous Alone? On the Challenge of Anonymity in Cryptographic Currencies Abstract: Cryptographic currencies balance two seemingly contradictory goals: radical transparency of the ledger and anonymity of users. A variety of privacy-enhancing techniques have been developed to strengthen users’ anonymity using obfuscation and cryptography, yet to this day their adoption remains limited. A closer look at existing deployments shows how in practice these techniques often fall short of their promises due to inherent complexity and dependence on user behavior. One such example is Monero, a privacy-centric cryptocurrency that allows users to obfuscate the true origin of the coin they are spending by including other chaff coins. Analyzing the Monero blockchain, we found several significant weaknesses in it’s design that allowed to retroactively deanonymize users’ transactions. This is one further example that cryptocurrency privacy is hard: the data is necessarily public forever, and often a user’s actions can negatively influence the anonymity of others. Based on the insights from the analysis of Monero and other privacy-preserving techniques, I’ll put forward a set of recommendations for protocol designers and discuss options for policy makers to regulate cryptographic currencies based on their inherent linkability. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Lauren Pick General Exam Presentation - Thursday, May 10, 2018 at 3:30 pm - CS401
by Barbara A. Mooring 10 May '18

10 May '18

Lauren Pick General Exam Presentation - Thursday, May 10, 2018 at 3:30 pm - CS401 Title: Exploiting Synchrony and Symmetry in Relational Verification Abstract: Relational safety specifications describe multiple runs of the same program or relate the behaviors of multiple programs. Approaches to automatic relational verification often compose the programs and analyze the composition for safety, but a naively composed program can lead to difficult verification problems. In my talk, I will demonstrate two ways of exploiting relational specifications to simplify verification subtasks generated during relational verification. First, I will describe an algorithm for maximizing opportunities for synchronizing code fragments containing loops, allowing a verification procedure to avoid computing difficult loop invariants. Second, I will show how to compute symmetries in the specifications, allowing a verification procedure to avoid redundant subtasks. These enhancements have been implemented in a prototype for verifying k-safety properties on Java programs, and evaluation confirms that taking advantage of relational specifications as described leads to a consistent performance speedup on a range of benchmarks. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Kyle Genova - General Exam Presentaton, Thursday, May 10, 2018 at 2:30 pm - CS402
by Barbara A. Mooring 10 May '18

10 May '18

Kyle Genova - General Exam Presentaton, Thursday, May 10, 2018 at 2:30 pm - CS402 Title: Unsupervised Training for 3D Morphable Model Regression Abstract: We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed on-the-fly by rendering the predicted faces with a differentiable renderer. To make training from features feasible and avoid network fooling effects, we introduce three objectives: a batch distribution loss that encourages the output distribution to match the distribution of the morphable model, a loopback loss that ensures the network can correctly reinterpret its own output, and a multi-view identity loss that compares the features of the predicted 3D face and the input photograph from multiple viewing angles. We train a regression network using these objectives, a set of unlabeled photographs, and the morphable model itself, and demonstrate state-of-the-art results. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Robert MacDavid General Exam Presentation - Thursday, May 10, 2018 at 12:00 pm, CS302
by Barbara A. Mooring 10 May '18

10 May '18

Robert MacDavid General Exam Presentation - Thursday, May 10, 2018 at 12:00 pm, CS302 Title: Scaling Network Polices in the Fast Path with MACH-4 Abstract: As network speeds increase, operators desire to push applications like Firewalls, Network Address Translators, and Load Balancers into network hardware for better bandwidth, latency, and aggregation. Emerging programmable switches are accelerating this trend, allowing operators to define their own functions that can run entirely in the hardware fast path. Unfortunately, such modern switches are still severely limited by memory, only capable of storing hundreds of thousands of application rules, where modern applications require tens of millions. In this work we show that, by using intimate knowledge of the programmable switches, centralized applications can be split across multiple switches for little to no performance penalty, allowing applications to scale with the size of the network. We show how application caches in each switch can be updated without software intervention, greatly reducing latency and control-plane load as compared to other caching solutions. Barbara A. Mooring Interim Graduate Coordinator Computer Science Department Princeton University

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Colloquium Speaker-Huacheng Yu, TODAY at 12:30
by Emily C. Lawrence 08 May '18

08 May '18

Huacheng Yu from Harvard University Tuesday, May 8, 2018 - 12:30pm Computer Science - Room 105 Host: Ran Raz "Better understanding of efficient dynamic data structures" Abstract: Data structures have applications and connections to algorithm design, database systems, streaming algorithms and other areas of computer science. Understanding what efficient data structures can do (and what they cannot do) is crucial to these applications. In this talk, I will present my work in analyzing efficient data structures and proving what they cannot accomplish. I will focus on the recent development in building new connections between dynamic data structures and communication complexity, as well as a new approach to analyzing dynamic data structures with Boolean outputs and super-logarithmic time. Bio: Huacheng Yu is a postdoctoral researcher in the Theory of Computing group at Harvard University. He obtained his PhD from Stanford University in 2017 under the supervision of Ryan Williams and Omer Reingold . He also holds a Bachelor's degree from Tsinghua University (2012). His primary research interests are data structure lower bounds. He also works in algorithm design and communication complexity.

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