Short Bio. Applied Mathematics in Computer Security represents my main field of interest. In particular I am mostly interested in Cryptography (from a theoretical point of view) and Cryptanalysis (from a computer science point of view).
During my education I have also studied many field about Computer Science that vary from the Computability and Complexity Analysis to the most recent logical theories (like λ-calculus, Linear Logic, . . . ).
More recently I have broaden my horizons by confront myself with new perspectives of the Applied Mathematics like Graph Theory, Computational Biology and Topology, Machine Learning and Big Data Analysis.
On tweakable black-box polinomials: the cube attacks family
On tweakable black-box polinomials: the cube attacks family
Monday 28th February 2021, 11:00 -- 12:00, Room 211 Pal. C
In 2009, Dinur and Shamir proposed the cube attack, an algebraic cryptanalysis technique that only requires black box access to a target cipher.Since then, this attack has received both many criticisms and endorsements from crypto community.
In this talk we review the cube attacks basics by means of a novel notation recently introduced by Onofri and Pedicini and we collect the many attacks that have been proposed starting from it. We provide a categorisation of these attacks in five classes: for each class, we provide a brief summary description along with the most recent cryptanalysis results.
Ref. Cianfriglia M., Onofri E., Onofri S., Pedicini M., Ten years of cube attacks [eprint:2022/137]
In this talk we review the cube attacks basics by means of a novel notation recently introduced by Onofri and Pedicini and we collect the many attacks that have been proposed starting from it. We provide a categorisation of these attacks in five classes: for each class, we provide a brief summary description along with the most recent cryptanalysis results.
Ref. Cianfriglia M., Onofri E., Onofri S., Pedicini M., Ten years of cube attacks [eprint:2022/137]
Attribute-based colouring and its applications in reducing problem's size
Attribute-based colouring and its applications in reducing problem's size
Monday 24th January 2021, 11:00 -- 12:00, Room 311 Pal. C
Graph structures are nowadays pervasive in Big Data. It is often useful to regroup such data in clusters, according to distinctive node features, and use a representative element for each cluster. In many real-world cases, clusters can be identified by a set of connected vertices that share the result of some categorical function, i.e. a mapping of the vertices into some categorical representation that takes values in a finite set C. As an example, we can identify contiguous terrains with the same discrete property on a geographical map, leveraging Space Syntax. In this case, thematic areas within cities are labelled with different colors and color zones are analysed by means of their structure and their mutual interactions. Contracted graphs can help identify issues and characteristics of the original structures that were not visible before.
This talk introduces and discusses the problem of contracting possibly large colored graphs into much smaller representatives. It also describe a novel serial but parallelizable algorithm to tackle this task. Some initial performance plots are given and discussed together with hints for future development.
Ref. Lombardi F., Onofri E., Graph Contraction on Attribute-Based Coloring [DOI:10.1016/j.procs.2022.03.056 ]
This talk introduces and discusses the problem of contracting possibly large colored graphs into much smaller representatives. It also describe a novel serial but parallelizable algorithm to tackle this task. Some initial performance plots are given and discussed together with hints for future development.
Ref. Lombardi F., Onofri E., Graph Contraction on Attribute-Based Coloring [DOI:10.1016/j.procs.2022.03.056 ]
The role of the applied mathematician: optimisation of pedestrian flows in crowded museums
The role of the applied mathematician: optimisation of pedestrian flows in crowded museums
Monday 13th December 2021, 11:00 -- 12:00, Room 211 Pal. C
Individual tracking of museum visitors based on portable radio beacons, an asset for behavioural analyses and comfort/performance improvements, is seeing increasing diffusion. Conceptually, this approach enables room-level localisation based on a network of small antennas (thus, without invasive modification of the existent structures). The antennas measure the intensity (RSSi) of self-advertising signals broadcasted by beacons individually assigned to the visitors. The signal intensity provides a proxy for the distance to the antennas and thus indicative positioning. However, RSSi signals are well-known to be noisy, even in ideal conditions (high antenna density, absence of obstacles, absence of crowd, . . . ).
In this talk, we present a method to perform accurate RSSi-based visitor tracking when the density of antennas is relatively low, e.g. due to technical constraints imposed by historic buildings. We combine an ensemble of "simple" localisers, trained based on ground-truth, with an encoding of the museum topology in terms of a total-coloured graph. This turns the localisation problem into a cascade process, from large to small scales, in space and in time. Our use case is visitors tracking in Galleria Borghese, Rome (Italy), for which our method manages >96% localisation accuracy, significantly improving on our previous work (J. Comput. Sci. 101357, 2021).
Ref. Onofri E., Corbetta A., RSSi-Based Visitor Tracking in Museums via Cascaded AI Classifiers and Coloured Graph Representations [DOI:10.17815/CD.2021.131]
In this talk, we present a method to perform accurate RSSi-based visitor tracking when the density of antennas is relatively low, e.g. due to technical constraints imposed by historic buildings. We combine an ensemble of "simple" localisers, trained based on ground-truth, with an encoding of the museum topology in terms of a total-coloured graph. This turns the localisation problem into a cascade process, from large to small scales, in space and in time. Our use case is visitors tracking in Galleria Borghese, Rome (Italy), for which our method manages >96% localisation accuracy, significantly improving on our previous work (J. Comput. Sci. 101357, 2021).
Ref. Onofri E., Corbetta A., RSSi-Based Visitor Tracking in Museums via Cascaded AI Classifiers and Coloured Graph Representations [DOI:10.17815/CD.2021.131]