[cs-talks] Upcoming CS Seminars: BUSec (Weds) + PhD Proposal (Tues)

Conroy, Nora Mairead conroynm at bu.edu
Tue Feb 10 19:36:18 EST 2015


BUSec Seminar
Tamper Detection and Continuous Non-Malleable Codes
Zahra Jafargholi, NEU
Wednesday, February 11, 2015 at 9:30am in MCS 180 – Hariri Institute

Abstract: We consider a public and keyless code $(\Enc,\Dec)$ which is used to encode a message $m$ and derive a codeword $c = \Enc(m)$. The codeword can be adversarially tampered via a function $f \in \F$ from some tampering function family $\F$, resulting in a tampered value $c' = f(c)$. We study the different types of security guarantees that can be achieved in this scenario for different families $\F$ of tampering attacks.

Firstly, we initiate the general study of tamper-detection codes, which must detect that tampering occurred and output $\Dec(c') = \bot$. We show that such codes exist for any family of functions $\F$ over $n$ bit codewords, as long as $|\F| < 2^{2^n}$ is sufficiently smaller than the set of all possible functions, and the functions $f \in \F$ are further restricted in two ways: (1) they can only have a few fixed points $x$ such that $f(x)=x$, (2) they must have high entropy of $f(x)$ over a random $x$. Such codes can also be made efficient when $|\F| = 2^{\poly(n)}$. For example, $\F$ can be the family of all low-degree polynomials excluding constant and identity polynomials. Such tamper-detection codes generalize the algebraic manipulation detection (AMD) codes of Cramer et al. (EUROCRYPT '08).

Next, we revisit non-malleable codes, which were introduced by Dziembowski, Pietrzak and Wichs (ICS '10) and require that $\Dec(c')$ either decodes to the original message $m$, or to some unrelated value (possibly $\bot$) that doesn't provide any information about $m$. We give a modular construction of non-malleable codes by combining tamper-detection codes and leakage-resilient codes. This gives an alternate proof of the existence of non-malleable codes with optimal rate for any family $\F$ of size $|\F| < 2^{2^n}$, as well as efficient constructions for families of size $|\F| = 2^{\poly(n)}$.

Finally, we initiate the general study of continuous non-malleable codes, which provide a non-malleability guarantee against an attacker that can tamper a codeword multiple times. We define several variants of the problem depending on: (I) whether tampering is persistent and each successive attack modifies the codeword that has been modified by previous attacks, or whether tampering is non-persistent and is always applied to the original codeword, (II) whether we can self-destruct and stop the experiment if a tampered codeword is ever detected to be invalid or whether the attacker can always tamper more. In the case of persistent tampering and self-destruct (weakest case), we get a broad existence results, essentially matching what's known for standard non-malleable codes. In the case of non-persistent tampering and no self-destruct (strongest case), we must further restrict the tampering functions to have few fixed points and high entropy. The two intermediate cases correspond to requiring only one of the above two restrictions.

These results have applications in cryptography to related-key attack (RKA) security and to protecting devices against tampering attacks without requiring state or randomness.

Joint work with Daniel Wichs.


Do-Not-Track and the Economics of Third-Party Advertising
Georgios Zervas, BU
Junior Faculty Fellow, Hariri Institute for Computing
Assistant Professor of Marketing, School of Management
Wednesday, February 11, 2015 at 3pm in MCS 180 - Hariri

Abstract: Retailers regularly target users with online ads based on their web browsing activity, benefiting both the retailers, who can better reach potential customers, and content providers, who can increase ad revenue by displaying more effective ads. The effectiveness of such ads relies on third-party brokers that maintain detailed user information, prompting legislation such as do-not-track that would limit or ban the practice. We gauge the economic costs of such privacy policies by analyzing the anonymized web browsing histories of 14 million individuals. We find that only 3% of retail sessions are currently initiated by ads capable of incorporating third-party information, a number that holds across market segments, for online-only retailers, and under permissive click-attribution assumptions. Third-party capable advertising is shown by 12% of content providers, accounting for 32% of their page views; this reliance is concentrated in online publishing (e.g., news outlets) where the rate is 91%. We estimate that most of the top 10,000 content providers could generate comparable revenue by switching to a “freemium” model, in which loyal site visitors are charged $2 (or less) per month. We conclude that do-not-track legislation would impact, but not fundamentally fracture, the Internet economy.

Bio: Georgios Zervas is an Assistant Professor of Marketing at Boston University’s School of Management. Before joining BU in 2013 he was a Simons postdoctoral fellow at Yale and an affiliate at the Center for Research on Computation and Society at Harvard. He received his PhD in Computer Science in 2011 from Boston University. He is broadly interested in understanding the strategic interactions of firms and consumers participating in internet markets using large-scale data collection and econometric analysis.

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UPCOMING

Ph.D Proposal
Learning Space-Time Structures for Human Action Recognition and Localization
Shugao Ma, BU
Tuesday, February 17, 2015 at 3:30pm in MCS 148

Abstract: In this thesis we study the problem of action recognition and localization in realistic video clips. In training only the action class labels of the training video clips are available, and in testing both the action label and the spatial location of the action performer(s) are to be predicted. Although many past works have been done, this remains a challenging problem due to the complexity of the human actions and the large intra-class variations. Human actions are inherently structured patterns of body movements. However, past works are inadequate in learning the space-time structures in human actions and leveraging them for action recognition. In this thesis we propose new methods that exploit such space-time structures for effective human action recognition and localization. In the feasibility study, we developed a new local space-time representation for action recognition and localization, the hierarchical Space-Time Segments . Using this new representation, we explored ensembles of hierarchical spatio-temporal trees, discovered directly from training data, to model these structures for action recognition. This proposed approach in the feasibility study achieved state-of-the-art performances on two challenging benchmark datasets UCF-Sports and HighFive. However, further works are needed to more efficiently discover space-time structures and better handle large scale data. In the remaining work, we will explore deep convolutional neural network (CNN) for larger scale action recognition problem, studying ensemble of CNN models trained on whole video frames blended with motion information and CNN models trained on automatically proposed foreground regions. We will also explore sub-graph vectorization method that can effectively encode space-time structures of human actions into vectors, which will enable us to efficiently discover discriminative structures in a vector space. We will evaluate the remaining works on larger scale dataset, e.g. , the UCF101 dataset that has 101 action classes and  13000 videos.


BUSec Seminar
Machine Learning Classification over Encrypted Data
Raphael Bost, DGA MI/Université de Rennes 1
Wednesday, February 18, 2015 at 9:30am in MCS 180 – Hariri Institute

Abstract:  Machine learning classification is used in numerous settings nowadays, such as medical or genomics predictions, spam detection, face recognition, and financial predictions. Due to privacy concerns, in some of these applications, it is important that the data and the classifier remain confidential.

In this work, we construct three major classification protocols that satisfy this privacy constraint: hyperplane decision, Naïve Bayes, and decision trees. We also enable these protocols to be combined. At the basis of these constructions is a new library of building blocks for constructing classifiers securely; we demonstrate that this library can be used to construct other classifiers as well, such as a multiplexer and a face detection classifier.

We implemented and evaluated our library and classifiers. Our protocols are efficient, taking milliseconds to a few seconds to perform a classification when running on real medical datasets.

Joint work with Raluca Ada Popa, Stephen Tu and Shafi Goldwasser which will appear in NDSS'15.


IVC Seminar
Computational Understanding of Image Memorability
Zoya Bylinskii, MIT
Thursday, February 19, 2015 at 4pm in MCS 148

Abstract: In this talk, I will describe the research done in the Oliva Lab on Image Memorability - a quantifiable property of images that can be used to predict whether an image will be remembered or forgotten. Apart from presenting the lab's research directions and findings, I will focus on the work I have done in understanding and modeling the intrinsic and extrinsic factors that affect image memorability. I will present results on how consistent people are in which images they find memorable and forgettable (across experiments, settings, and visual stimuli) and I will show how these findings generalize to information visualizations. I will also demonstrate how the extrinsic factors of image context and observer eye behavior modulate image memorability. I will present an information-theoretic model of context and image distinctiveness, to quantify their effects on memorability. Finally, I will demonstrate how eye movements, pupil dilations, and blinks can be predictive of image memorability. In particular, our computational model can use an observer's eye movements on an image to predict whether or not the image will be later remembered. In this talk, I hope to offer a more complete picture of image memorability, including the contributions to cognitive science, and the computational applications made possible.

The following is the first paper on image memorability that has come out of the Oliva Lab, and has started a whole direction of research: http://cvcl.mit.edu/papers/IsolaXiaoTorralbaOliva-PredictingImageMemory-CVPR2011.pdf -- it can give people some background, though I will provide an intro as well.

Bio: Zoya Bylinskii is a PhD student at MIT, jointly supervised by Aude Oliva and Fredo Durand. She works in the area of computational perception - at the intersection of cognitive science and computer science. Specifically, she is interested in studying human memory and attention, in order to build computational models to advance the understanding and application possibilities of these areas. Her current work spans a number of research directions, including: image memorability, saliency benchmarking, and information visualizations. Zoya most recently completed her MS under the supervision of Antonio Torralba and Aude Oliva, on a "Computational Understanding of Image Memorability". Prior to this, her BS research on parts-based object recognition was supervised by Sven Dickinson at the University of Toronto. She also spent a lovely summer in 2011 working in BU with Stan Sclaroff on reduplication detection in sign language :)
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