Anup K Ghosh

One of the hardest challenges for companies and their officers is determining how much to spend on cybersecurity and the appropriate allocation of those resources. Security “investments” are a cost on the ledger, and as such, companies do not want to spend more on security than they have to. The question most boards have is “how much security is enough?” and “how good is our security program?” Most CISOs and SOC teams have a hard time answering these questions for a lack of data and framework to measure risk and compare with other similar sized companies. This paper presents a data-driven practical approach to assessing and scoring cybersecurity risk that can be used to allocate resources efficiently a nd mitigate cybersecurity risk in areas that need it the most. We combine both static and dynamic measures of risk to give a composite score more indicative of cybersecurity risk over static measures alone.

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PPA: Preference Profiling Attack Against Federated Learning

Chunyi Zhou (Nanjing University of Science and Technology), Yansong Gao (Nanjing University of Science and Technology), Anmin Fu (Nanjing University of Science and Technology), Kai Chen (Chinese Academy of Science), Zhiyang Dai (Nanjing University of Science and Technology), Zhi Zhang (CSIRO's Data61), Minhui Xue (CSIRO's Data61), Yuqing Zhang (University of Chinese Academy of Science)

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FUZZILLI: Fuzzing for JavaScript JIT Compiler Vulnerabilities

Samuel Groß (Google), Simon Koch (TU Braunschweig), Lukas Bernhard (Ruhr-University Bochum), Thorsten Holz (CISPA Helmholtz Center for Information Security), Martin Johns (TU Braunschweig)

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Towards Privacy-Preserving Platooning Services by means of Homomorphic Encryption

Nicolas Quero (Expleo France), Aymen Boudguiga (CEA LIST), Renaud Sirdey (CEA LIST), Nadir Karam (Expleo France)

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