Gleich 2 Artikel mit SSI-Beteiligung wurden zur Publikation auf der USENIX Security 2025 akzeptiert!
- F. Mazzone, M. Everts, F. Hahn, und A. Peter, „Efficient Ranking, Order Statistics, and Sorting under CKKS”
- M. Buechel, T. Paladini, S. Longari, M. Carminati, S. Zanero, H. Binyamini, G. Engelberg, D. Klein, G. Guizzardi, M. Caselli, A. Continella, M. van Steen, A. Peter, und T. van Ede, "SoK: Automated TTP Extraction from CTI Reports – Are We There Yet?"
Kurze Zusammenfassungen (auf Englisch):
Artikel 1: Fully Homomorphic Encryption (FHE) enables operations on encrypted data, making it extremely useful for privacy-preserving applications, especially in cloud computing environments. In such contexts, operations like ranking, order statistics, and sorting are fundamental functionalities often required for database queries or as building blocks of larger protocols. However, the high computational overhead and limited native operations of FHE pose significant challenges for an efficient implementation of these tasks. These challenges are exacerbated by the fact that all these functionalities are based on comparing elements, which is a severely expensive operation under encryption.
Previous solutions have typically based their designs on swap-based techniques, where two elements are conditionally swapped based on the results of their comparison. These methods aim to reduce the primary computational bottleneck: the comparison depth, which is the number of non-parallelizable homomorphic comparisons in the algorithm. The current state of the art solutions for sorting by Lu et al. (IEEE S&P '21) and Hong et al. (IEEE TIFS 2021), for instance, achieve a comparison depth of log^2 N and k logk^2N, respectively.
In this paper, we address the challenge of reducing the comparison depth by shifting away from the swap-based paradigm. We present solutions for ranking, order statistics, and sorting, that achieve a comparison depth of up to 2 (constant), making our approach highly parallelizable and suitable for hardware acceleration. Leveraging the SIMD capabilities of the CKKS FHE scheme, our approach re-encodes the input vector under encryption to allow for simultaneous comparisons of all elements with each other. The homomorphic re-encoding incurs a minimal computational overhead of O(log N) rotations. Experimental results show that our approach ranks a 128-element vector in approximately 5.76s, computes its argmin/argmax in 12.83s, and sorts it in 78.64s.
Artikel 2: Cyber Threat Intelligence (CTI) plays a critical role in sharing knowledge about new and evolving threats. With the increased prevalence and sophistication of threat actors, intelligence has expanded from simple indicators of compromise to extensive CTI reports describing high-level attack steps known as Tactics, Techniques and Procedures (TTPs). Such TTPs, often classified into the ontology of the MITRE ATT&CK framework, make CTI significantly more valuable, but also harder to interpret and automatically process. Natural Language Processing (NLP) makes it possible to automate large parts of the knowledge extraction from CTI reports; over 40 papers discuss approaches, ranging from named entity recognition over embedder models to generative large language models. Unfortunately, existing solutions are largely incomparable as they consider decisively different and constrained settings, rely on custom TTP ontologies, and use a multitude of custom, inaccessible CTI datasets. We take stock, systematize the knowledge in the field, and empirically evaluate existing approaches in a unified setting for fair comparisons. We gain several fundamental insights, including (1) the finding of a kind of performance limit that existing approaches seemingly cannot overcome as of yet, (2) that traditional NLP approaches (possibly counterintuitively) outperform modern embedder-based and generative approaches in realistic settings, and (3) that further research on understanding inherent ambiguities in TTP ontologies and on the creation of qualitative datasets is key to take a leap in the field.