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Software Seminar Series (S3)

Martin Ceresa

Tuesday, December 1, 2020

11:00am https://zoom.us/j/3911012202 (pass: s3)

Martin Ceresa, PhD Student, IMDEA Software Institute

Effectful Improvement Theory

Abstract:

Optimising programs is hard. Not only one must preserve semantics, but also one needs to ensure that an optimisation really makes the program better. The first part, preserving semantics, has been, and still is, the subject of much research. We follow a line of work that starts with Morris' observational equivalence, continues with Abramsky's applicative bisimilarity and Howe's method, and concludes in a recent abstract formalization of applicative bisimilarity in the presence of algebraic effects by Dal Lago, Gavazzo and Levy. The second part is a path less traveled, with the improvement theory of Sands being the most prominent example. In this work, we connect these two parts, by obtaining an abstract Theory of Improvements based on effectful applicative bisimilarity that extends Sands' notion of improvement to effectful languages.


Time and place:
11:00am https://zoom.us/j/3911012202 (pass: s3)
IMDEA Software Institute, Campus de Montegancedo
28223-Pozuelo de Alarcón, Madrid, Spain


Dimitris Kolonelos

Tuesday, November 24, 2020

11:00am https://zoom.us/j/3911012202 (pass: s3)

Dimitris Kolonelos, PhD Student, IMDEA Software Institute

Incrementally Aggregatable Vector Commitments and Applications to Verifiable Decentralized Storage

Abstract:

Vector commitments with subvector openings (SVC) [Lai-Malavolta, Boneh-Bunz-Fisch; CRYPTO’19] allow one to open a committed vector at a set of positions with an opening of size independent of both the vector’s length and the number of opened positions. We continue the study of SVC with two goals in mind: improving their efficiency and making them more suitable to decentralized settings. We address both problems by proposing a new notion for VC that we call incremental aggregation and that allows one to merge openings in a succinct way an unbounded number of times. We show two applications of this property. The first one is immediate and is a method to generate openings in a distributed way. For the second one, we use incremental aggregation to design an algorithm for faster generation of openings via preprocessing. We then proceed to realize SVC with incremental aggregation. We provide two constructions in groups of unknown order that, similarly to that of Boneh et al. (which supports only one-hop aggregation), have constant-size public parameters, commitments and openings. As an additional feature, for the first construction we propose efficient arguments of knowledge of subvector openings which immediately yields a keyless proof of storage with compact proofs. Finally, we address a problem closely related to that of SVC: storing a file efficiently in completely decentralized networks. We introduce and construct verifiable decentralized storage (VDS), a crypto-graphic primitive that allows to check the integrity of a file stored by a network of nodes in a distributed and decentralized way. Our VDS constructions rely on our new vector commitment techniques. This is joint work with Matteo Campanelli, Dario Fiore, Nicola Greco and Luca Nizzardo. It'll appear next month at Asiacrypt but it's not a strict rehearsal.


Time and place:
11:00am https://zoom.us/j/3911012202 (pass: s3)
IMDEA Software Institute, Campus de Montegancedo
28223-Pozuelo de Alarcón, Madrid, Spain


Gibran Gómez

Tuesday, October 27, 2020

11:00am https://zoom.us/j/3911012202 (pass: s3)

Gibran Gómez, PhD Student, IMDEA Software Institute

Malicious TLS Traffic Detection using Unsupervised Machine Learning

Abstract:

Transport Layer Security (TLS) is utilized by several applications to secure network communication through encryption. Malware adoption of TLS is rapidly growing, disabling widespread approaches for detection on-the-wire that require to have access to plain-text contents of network communications to characterize malicious traffic. Due to traffic decryption disrupts privacy for all other types of communication (for instance, by using a Man-in-the-Middle approach), different supervised machine learning based strategies have been developed to build malware detectors directly from TLS metadata. Although, such solutions work just for a small subset of labeled samples. In this talk we're going to present an unsupervised approach, that doesn't have such limitation. Instead, it can be applied to labeled or unlabeled samples to cluster similar TLS flows, allowing to produce a model able to make predictions on previously unseen traces from a larger number of malware families.


Time and place:
11:00am https://zoom.us/j/3911012202 (pass: s3)
IMDEA Software Institute, Campus de Montegancedo
28223-Pozuelo de Alarcón, Madrid, Spain


Software Seminar Series (S3) - Spring 2020