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Cyber risk management is a very important problem for every company connected to the internet. Usually, risk management is done considering only Risk Analysis without connecting it with Vulnerability Assessment, using external and expensive tools. In this paper we present CYber Risk Vulnerability Management (CYRVM)-a custom-made software platform devised to simplify and improve automation and continuity in cyber security assessment. CYRVM's main novelties are the combination, in a single and easy-to-use Web-based software platform, of an online Vulnerability Assessment tool within a Risk Analysis framework following the NIST 800-30 Risk Management guidelines and the integration of predictive solutions able to suggest to the user the risk rating and classification.

We present a system that automatically generates a cycle-accurate and bit-true Instruction Level Simulator (ILS) and a hardware implementation model given a description of a target processor. An ILS can be used to obtain a cycle count for a given program running on the target architecture, while the cycle length, die size, and power consumption can be obtained from the hardware implementation model. These figures allow us to accurately and rapidly evaluate target architectures within an architecture exploration methodology for system-level synthesis.In an architecture exploration scheme, both the ILS and the hardware model must be generated automatically, else a substantial programming and hardware design effort has to be expended in each design iteration. Our system uses the ISDL machine description language to support the automatic generation of the ILS and the hardware synthesis model, as well as other related tools.

Collaborative recommending systems aim to predict a potential user‐item rating on the basis of remaining ones. Since, in several contexts, sharing of other users' ratings may be prevented by confidentiality concerns, several works have effectively addressed the design of privacy preserving recommenders. Still, most of the proposed solutions rely on advanced cryptographic methodologies, whose may conflict with the simplicity and viability requirements of real world deployments. In contrast, we propose an approach which does not require any complex cryptography. We show that whenever we can tolerate recommendations based on average values, we can transform the recommender into a privacy‐preserving one, by using two noncolluding replicas of the same system, and by distributing randomly “blinded” data to these replicas. To protecting each user's rating, a key asset of our approach is the ability to conceal which specific items are rated by which users. Our proposal is secure under the honest‐but‐curious attacker's assumption, and we show how it can be extended to guarantee robustness also against malicious adversaries. Finally, as a proof‐of‐concept, we present an implementation of the proposed approach for our motivating use case—collaborative assessment of computer/network vulnerabilities without revealing which of them affect one own infrastructure.