Visible to the public Biblio

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2023-01-13
Masago, Hitoshi, Nodaka, Hiro, Kishimoto, Kazuma, Kawai, Alaric Yohei, Shoji, Shuichi, Mizuno, Jun.  2022.  Nano-Artifact Metrics Chip Mounting Technology for Edge AI Device Security. 2022 17th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT). :1—4.
In this study, the effect of surface treatment on the boding strength between Quad flat package (QFP) and quartz was investigated for establishing a QFP/quartz glass bonding technique. This bonding technique is necessary to prevent bond failure at the nano-artifact metrics (NAM) chip and adhesive interface against physical attacks such as counterfeiting and tampering of edge AI devices that use NAM chips. Therefore, we investigated the relationship between surface roughness and tensile strength by applying surface treatments such as vacuum ultraviolet (VUV) and Ar/O2 plasma. All QFP/quartz glass with surface treatments such as VUV and Ar/O2 plasma showed increased bond strength. Surface treatment and bonding technology for QFP and quartz glass were established to realize NAM chip mounting.
2022-01-31
Sjösten, Alexander, Hedin, Daniel, Sabelfeld, Andrei.  2021.  EssentialFP: Exposing the Essence of Browser Fingerprinting. 2021 IEEE European Symposium on Security and Privacy Workshops (EuroS PW). :32—48.
Web pages aggressively track users for a variety of purposes from targeted advertisements to enhanced authentication. As browsers move to restrict traditional cookie-based tracking, web pages increasingly move to tracking based on browser fingerprinting. Unfortunately, the state-of-the-art to detect fingerprinting in browsers is often error-prone, resorting to imprecise heuristics and crowd-sourced filter lists. This paper presents EssentialFP, a principled approach to detecting fingerprinting on the web. We argue that the pattern of (i) gathering information from a wide browser API surface (multiple browser-specific sources) and (ii) communicating the information to the network (network sink) captures the essence of fingerprinting. This pattern enables us to clearly distinguish fingerprinting from similar types of scripts like analytics and polyfills. We demonstrate that information flow tracking is an excellent fit for exposing this pattern. To implement EssentialFP we leverage, extend, and deploy JSFlow, a state-of-the-art information flow tracker for JavaScript, in a browser. We illustrate the effectiveness of EssentialFP to spot fingerprinting on the web by evaluating it on two categories of web pages: one where the web pages perform analytics, use polyfills, and show ads, and one where the web pages perform authentication, bot detection, and fingerprinting-enhanced Alexa top pages.