News
Next Seminar on 28.08.2024
Written on 22.08.2024 11:42 by Xinyi Xu
Dear All,
The next seminar(s) will take place on 2024-08-28 at 14:30 (Session A) and 14:00 (Session B).
Session A: (14:30 - 15:00)
Demian Fink
https://cispa-de.zoom.us/j/96786205841?pwd=M3FOQ3dSczRabDNLb3F1czVXVUpvdz09
Meeting-ID: 967 8620 5841
Password: BT!u5=
Session B: (14:00 - 14:30, 14:30 - 15:00, 15:00 - 15:30)
Yasin Esfandiari, Eduard Ebert, Christian Bryan Marcelino
https://cispa-de.zoom-x.de/j/66136901453?pwd=YVBSZU9peUpvUlk4bWp3MDR4cGlUUT09
Meeting-ID: 661 3690 1453
Password: sxHhzA004}
Session A
14:30 - 15:00
Speaker: Demian Fink
Type of Talk: Bachelor Intro
Advisor: Matthias Fassl
Title: Comparing Security and Privacy Advice on Social Media with established Expert Advice
Research Area: RA5: Empirical and Behavioural Security
Abstract: The landscape of security and privacy advice on social media is large. Individual sites like Twitter (now know as X) were previously analysed, but no full scale analysis over most or all major platforms has been conducted. Understanding the whos, the what and even the whys of security advice can help shape the future of security advice of tomorrow. The goal of this thesis is to understand these questions of who, what and why by collecting security and privacy advice from a multitude of social media platforms such as Twitter (X), Instagram, TikTok, Reddit, and Youtube. Other than just collecting the substance of the post, authors are collected to classify them into groups such as "News Agency", "Popular Influencer" etc. and a form to meassure popularity such as likes, retweets or views. The data is then compared to a established expert advice.
Session B
14:00 - 14:30
Speaker: Yasin Esfandiari
Type of Talk: Master Intro
Advisor: Sebastian Stich
Title: Image-Quality-Likelihood trade-off in Diffusion Models
Research Area: RA1: Trustworthy Information Processing
Abstract: Usually, Diffusion Models are trained to optimize the sample quality, which leads to a worse likelihood. On the other hand, some methods are designed to get a good likelihood, but the sample quality is low. Though the objective is the same, only the weighting term differs for training in those models. In this thesis, we are looking for a method that gets us both the good likelihood(in terms of BPD) and the sample quality (in terms of FID) using pre-trained Diffusion Models.
14:30 - 15:00
Speaker: Eduard Ebert
Type of Talk: Bachelor Intro
Advisor: Lorenz Hetterich, Michael Schwarz
Title: Reverse Engineering the Stride Prefetcher
Research Area: RA3: Threat Detection and Defenses
Abstract: Modern processors use various optimizations to minimize the memory access latency. One such optimization is the hardware prefetcher, which aims to reduce the cache miss penalty. However, the microarchitectural hash functions used to index the prefetchers' internal data structures remain undocumented. Previous works have reversed hash functions used to select cache slices, map physical addresses to DRAM channels, DIMMs, ranks, and banks, or map virtual addresses to TLB sets. These efforts also enabled new attacks such as DRAMA or TLBleed or improved existing attacks such as Rowhammer. In this thesis, we reverse engineer the stride prefetcher on multiple microarchitectures and present a case study on Zen/Zen+.
15:00 - 15:30
Speaker: Christian Bryan Marcelino
Type of Talk: Bachelor Final
Advisor: Stella Wohnig, Nico Döttling
Title: Comparative Analysis of Range Proofs with Application to the McFly Time Release Protocol
Research Area: RA1: Trustworthy Information Processing
Abstract: McFly is a Time-lock Puzzle primitive created with the help of blockchain (Proof of Stake) finality layer. This makes McFly not computationally wasteful and with the help of constant production rate of blockchain, we can decide exactly when the puzzle is solved. A little shortcoming resides in the construction of McFly protocol: The message lies on the exponentiation. Therefore, McFly needs a range proof to make sure that the message lies on a certain range. The proof size of McFly protocol exceeds the preferred value, thus we want to consider finding another range proof. In this thesis, we will explore the state of the art of range proof protocols to improve the situation of McFly. The aim of this thesis is not only to find another range proof that is smaller in size, along with incorporating them into the McFly protocol.