Italy

In the current digital age, data is continually being collected by organizations and governments alike. While the goal is to use this data to derive insight and improve services, the ubiquitous collection and analysis of data creates a threat to privacy. Furthermore, the digitization and centralization of data creates attractive targets for cyber criminals, with security breaches harming both individuals and organizations. In this talk, we present a vision for how privacy-preserving analytics can be done in the big data environment which will require a combination of technological advances to ensure both process and output privacy as well as socio-cognitive approaches to ensure the widespread use and deployment of such work.

SECRYPT 2021

Privacy-Preserving Analytics in the Big Data Environment

 **RTSS 2021** has successfully concluded. Hope you have enjoyed it, and look forward to seeing you next year in person.
    Announcement for the Awards:
 * Best Paper Award goes to: Monte Carlo Response-Time Analysis, Sergey Bozhko, Georg von der Brüggen, and Björn Brandenburg
 * Best Student Paper Award goes to: Automatic Energy-Hotspot Detection and Elimination in Real-Time Deeply Embedded Systems, Mohsen Shekarisaz, Lothar Thiele, and Mehdi Kargahi.
 * Best Solution Award for Industry Session goes to: Toward Real-Time Guaranteed Scheduling for Autonomous Driving Systems. Jinghao Sun, Tianyi Wang, Kailu Duan, Bin Lu, Jiankang Ren, Zhishan Guo, and Guozhen Tan.



IEEE RTSS 2021

The IEEE Real-Time Systems Symposium (RTSS) is the premier conference in the field of real-time systems, and is a venue for researchers and practitioners to showcase innovations covering all aspects of real-time systems, including theory, design, analysis, implementation, evaluation, and experience. 

keynote

SECRYPT is an annual international conference covering research in information and communication security. The 18th International Conference on Security and Cryptography (SECRYPT 2021) presents novel research on all theoretical and practical aspects of data protection, privacy, security, and cryptography. 

There havebeen a handfulof ground-breaking concepts in access control over the past half century whichhave received significanttraction in practical deployments. These include the fundamental policy-mechanism and operational-administrative distinctions, alongwith the authorization models of discretionary access control (DAC), mandatory access control (MAC), role-based access control (RBAC), attribute-based access control (ABAC) and relationship-based access control (ReBAC). In this talk we will argue that modern cyber systems require an effective convergence of these concepts, in that they must coexist in mutuallysupportive synergy. We will highlight some challenges and opportunities in making this vision a practical reality.

Access Control Convergence: Challenges and Opportunities

We consider the problem of predicting cyber-attacks based on known Common Vulnerability & Exposure (CVE) numbers. Given a CVE, we wish to answer 3 questions: (i) Will the CVE be exploited by malicious hackers? (ii) If so, when? (iii) How severe will the attack be? The answers to these questions are critical for almost all companies with significant software/hardware investments, for manufacturers of those software/hardware components, and for governments of the nations involved. In this talk, I will primarily focus on when a vulnerability will be exploited. Using a 23-month dataset gleaned from 5 sources, I will present a novel family of CAT (CVE-Author-Tweet) graphs. Each CAT graph has a massive associated system of recursive equations whose solution yields ``popularity scores’’ for the CVE-Author-Tweet nodes in the graph. Using these scores for different CAT graphs, we show a model that can predict when a vulnerability will be exploited – and we will use real world case studies to illustrate the efficacy of the approach. The talk will briefly describe progress on problems (i) and (iii) as well.

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Privacy-Preserving Analytics in the Big Data Environment

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