Provably Robust Decisions based on Potentially Malicious Sources of Information

التفاصيل البيبلوغرافية
العنوان: Provably Robust Decisions based on Potentially Malicious Sources of Information
المؤلفون: Jun Sun, Tim Muller, Dongxia Wang
المصدر: CSF
بيانات النشر: IEEE, 2020.
سنة النشر: 2020
مصطلحات موضوعية: Computer science, media_common.quotation_subject, Cognition, 02 engineering and technology, Computer security, computer.software_genre, Robustness (computer science), 020204 information systems, Honesty, 0202 electrical engineering, electronic engineering, information engineering, Information system, Malware, 020201 artificial intelligence & image processing, Special case, computer, media_common
الوصف: Sometimes a security-critical decision must be made using information provided by peers. Think of routing messages, user reports, sensor data, navigational information, blockchain updates. Attackers manifest as peers that strategically report fake information. Trust models use the provided information, and attempt to suggest the correct decision. A model that appears accurate by empirical evaluation of attacks may still be susceptible to manipulation. For a security-critical decision, it is important to take the entire attack space into account. Therefore, we define the property of robustness: the probability of deciding correctly, regardless of what information attackers provide. We introduce the notion of realisations of honesty, which allow us to bypass reasoning about specific feedback. We present two schemes that are optimally robust under the right assumptions. The “majority-rule” principle is a special case of the other scheme which is more general, named “most plausible realisations”.
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_________::068ee36a02844b02802dfc874615f545
https://doi.org/10.1109/csf49147.2020.00036
حقوق: OPEN
رقم الأكسشن: edsair.doi...........068ee36a02844b02802dfc874615f545
قاعدة البيانات: OpenAIRE