BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Date iCal//NONSGML kigkonsult.se iCalcreator 2.20.2// METHOD:PUBLISH X-WR-CALNAME;VALUE=TEXT:Eventi DIAG BEGIN:VTIMEZONE TZID:Europe/Paris BEGIN:STANDARD DTSTART:20161030T030000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET END:STANDARD BEGIN:DAYLIGHT DTSTART:20170326T020000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:calendar.8116.field_data.0@oba.diag.uniroma1.it DTSTAMP:20260405T053345Z CREATED:20170127T122007Z DESCRIPTION:Abstract: Recent advance in Secure Multi-Party Computation (SMP C) and Homomorphic Encryption (HE) opened the way for several privacy pres erving applications. However such techniques have high complexity and need s to be used in a clever way. In this seminar an introduction to the topic and some possible applications are provided\, focusing principally in pri vacy preserving biometric authentication and privacy preserving consensus algorithm for decentralized information fusion. The first application aims to solve privacy issues related to the use of user's biometric in remote authentication protocols. Biometric data are inherent parts of a person’s body and cannot be replaced if they are compromised. Even worse\, compromi sed biometric data can be used to have access to sensitive information and to impersonate the victim for malicious purposes. Processing biometric si gnals in the encrypted domain provides a secure and elegant way to overcom e the aforementioned problems. Thanks to SMPC techniques\, it is possible to carry out the match between any two biometric templates by working only on encrypted data\, but the high complexity prevents their use in practic al applications\, hence a it is necessary that the underlying biometric pr ocessing algorithms and the STPC protocol are designed jointly by taking i nto account both the cryptographic and the signal processing facets of the problem. The second application focuses on the protection of sensor priva cy in distributed environments\, where a central coordination node is miss ing. In the area of Internet of Thing (IoT)\, to deal with a large amount of information\, and provide accurate measurements\, service providers ado pt information fusion\, which can be performed by means of consensus algor ithms. These algorithms allow distributed agents to (iteratively) compute linear functions on the exchanged data\, and take decisions based on the o utcome\, without the need for the support of a central entity. However\, d ata fusion raises several security concerns\, especially when private or s ecurity critical information are involved in the computation. ODIN is a no vel algorithm based on the popular consensus gossip algorithm that allows information fusion over encrypted data and prevents distributed agents hav e direct access neither to the data while they iteratively reach consensus \, nor the final consensus value\, but they can only retrieve partial info rmation\, e.g.\, a binary decision. Short bio: Riccardo Lazzeretti graduat ed with a degree in computer science engineering from the University of Si ena\, Italy\, in 2007\, where he continued his studies as a Ph.D. student under the supervision of Prof. Mauro Barni in the Information Engineering Department. From November 2009 to May 2010\, he was with Philips Lab in Ei ndhoven\, The Netherlands. In 2012\, he received a research grant and cont inued his research in the Information Engineering and Mathematics Departme nt of the University of Siena. From September 2016 he is continuing his re search activities in the Mathematical Department of the University of Padu a together with Prof. Conti. His research activity is mainly focused on pr ivacy-preserving applications based on secure two-party computation tools. He authored several scientific papers\, appeared in the proceedings of to p-level international conferences and journals\, and is regularly invited as a TPC member to the top-level conferences in his areas of interest. He is associate editor of 'Elsevier Journal of Information Security and Appli cations'\, Program Co-Chair of '51th IEEE International Carnahan Conferenc e on Security Technologies (ICCST 2017)' and ˆ Workshop chair of 'IEEE Int ernational Conference on Advanced and Trusted Computing (ATC 2017)'. He ha s been assistant in various courses at academic level. DTSTART;TZID=Europe/Paris:20170202T110000 DTEND;TZID=Europe/Paris:20170202T110000 LAST-MODIFIED:20190805T155749Z LOCATION:Aula Magna SUMMARY:Signal Processing in the Encrypted Domain for Privacy Preserving Ap plications - Riccardo Lazzaretti\n\n\n \n \n\n \n\n\nRiccardo\n\n\nLa zzeretti \n\n \n\n \n\n\n\n\n\nProfessore associato\n\nstanza: \n\nB1 14\n\ntelefono: \n\n+39 0677274141 \n\n \n\n \n\nBiografia: \n\n\n\nI got the MSc degree (Laurea) in Computer Science Engineering at the Univer sity of Siena in 2007. Then I continued my studies as a Ph.D. under the su pervision of Prof. Mauro Barni at the Information Engineering Department o f the University of Siena\, working on Signal Processing of Encrypted Sign als. During Ph.D.\, from November 2009 to May 2010\, I spent six months in Philips Lab at Eindhoven\, The Netherland. In 2012 I discussed the Ph.D. thesis. Until October 2015\, I continued my research activities as a post- Doc at the Information Engineering and Mathematics Department of the Unive rsity of Siena in the VIPP (Visual Information Processing and Protection) group\, led by Prof. Mauro Barni. In the group I supervised\, together wit h Prof. Barni\, the activities of master and PhD students involved in SPED (Signal Processing in the Encrypted Domain\, also named Secure Multi-Part y Computation) topics and actively take part in other research fields addr essed by the group\, such as forensic\, watermarking and adversarial signa l processing. Together with Prof. Barni\, University of Siena\, I am conti nuing the research in privacy preserving biometric authentication\, extend ing it to multi-biometrics. From November 2015 to January 2016\, I’ve been part of the research team of Cynny s.p.a.\, an Italian start-up\, where I was involved in machine learning problems\, such as object recognition\, face detection\, affective computing. I’m currently broadening my knowledg e in the topic\, by approaching deep learning and implementing Convolution al Neural Networks with Google’s TensorFlow. From September 2016 to Februa ry 2017\, I had a research grant at the University of Padua\, Italy\, supe rvised by Prof. Conti. I started several research activities together with Prof. Conti that still are on-going. First of all\, together with Prof Co nti and Scientist Braca of Centre for Maritime Research and Experimentatio n (CMRE)\, NATO\, we are working on the implementation of privacy preservi ng consensus algorithm for decentralized Information fusion in Internet of Things. Together with Prof. Conti and Michele Nati\, Digital Catapult\, U K\, we are exploring Blockchain-based architectures for smart health appli cations. Together with Prof. Conti and Quattrociocchi\, IMT Lucca\, Italy\ , we are working on the possibility of discriminating hoaxes in social net works from the analysis of only structural properties of content propagati on cascades. From March 2017 to August 2019 I have been an Assistant Profe ssor (RTD-A) at Sapienza University of Rome\, where I am principally invol ved in APT malware analysis. From September 2019 to August 2022  I have be en an Assistant Professor (RTD-B) at Sapienza University of Rome\, working in IoT security. Since September 2022 I am an Associate Professor at Sapi enza University of Rome.\n\n\nInteressi di ricerca: \n\nPrivacy preserving applications through Secure Multi-Party Computation protocols\nBiometric identification\nMalware analysis\nIoT security and Provacy\nNamed Data Net work (NDN) security\nSecurity and Provacy in Healthcare\n\n \n\nqualifica_ rr: \n\nAssociate professors URL;TYPE=URI:http://oba.diag.uniroma1.it/node/8116 END:VEVENT END:VCALENDAR