Thomas Schneider
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thoschneider.bsky.social
Thomas Schneider
@thoschneider.bsky.social
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https://www.thoschneider.de
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HE-SecureNet: An Efficient and Usable Framework for Model Training via Homomorphic Encryption (Thomas Schneider, Huan-Chih Wang, Hossein Yalame) ia.cr/2025/1591
Abstract. Energy-efficient edge devices are essential for the widespread deployment of machine learning (ML) services. However, their limited computational capabilities make local model training infeasible. While cloud-based training offers a scalable alternative, it raises serious privacy concerns when sensitive data is outsourced. Homomorphic Encryption (HE) enables computation directly on encrypted data and has emerged as a promising solution to this privacy challenge. Yet, current HE-based training frameworks face several shortcomings: they often lack support for complex models and non-linear functions, struggle to train over multiple epochs, and require cryptographic expertise from end users. We present HE-SecureNet, a novel framework for privacy-preserving model training on encrypted data in a single-client–server setting, using hybrid HE cryptosystems. Unlike prior HE-based solutions, HE-SecureNet supports advanced models such as Convolutional Neural Networks and handles non-linear operations including ReLU, Softmax, and MaxPooling. It introduces a level-aware training strategy that eliminates costly ciphertext level alignment across epochs. Furthermore, HE-SecureNet automatically converts ONNX models into optimized secure C++ training code, enabling seamless integration into privacy-preserving ML pipeline—without requiring cryptographic knowledge. Experimental results demonstrate the efficiency and practicality of our approach. On the Breast Cancer dataset, HE-SecureNet achieves a 5.2× speedup and 33% higher accuracy compared to ConcreteML (Zama) and TenSEAL (OpenMined). On the MNIST dataset, it reduces CNN training latency by 2× relative to Glyph (Lou et al., NeurIPS’20), and cuts communication overhead by up to 66× on MNIST and 42× on CIFAR-10 compared to MPC-based solutions. Image showing part 2 of abstract.
September 5, 2025 at 11:49 AM
thoschneider.bsky.social
Our @erc.europa.eu Starting Grant project Privacy-Preserving Services On The Internet (PSOTI) ended successfully. Its main results are summarized in this "Results in Brief" article. Thanks
@encrypto-group.bsky.social, @cs-tudarmstadt.bsky.social, @tuda.bsky.social! cordis.europa.eu/article/id/4...
A suite of privacy-enhancing technologies
New technologies and protocols help protect user data while allowing the service providers to securely process that data.,,,European...
cordis.europa.eu
June 23, 2025 at 1:56 PM
thoschneider.bsky.social
In this upcoming #PETS25 paper, we show how to privately and efficiently compute centrality metrics on multi-layer graphs. A motivating application is private analysis of social interactions on social media platforms. Our open-source implementation scales to graphs with 500k nodes and 5M edges.
eprint.ing.bot ePrint Updates @eprint.ing.bot · Apr 13
MultiCent: Secure and Scalable Centrality Measures on Multilayer Graphs (Andreas Brüggemann, Nishat Koti, Varsha Bhat Kukkala, Thomas Schneider) ia.cr/2025/652
Abstract. As real-world networks such as social networks and computer networks are often complex and distributed, modeling them as multilayer graphs is gaining popularity. For instance, when studying social interactions across platforms like LinkedIn, Facebook, TikTok, and Bluesky, users may be connected on several of these platforms. To identify important nodes/users, the platforms might wish to analyze user interactions using, e.g., centrality measures when accounting for connections across all platforms. That raises the challenge for platforms to perform such computation while simultaneously protecting their user data to shelter their own business as well as uphold data protection laws. This necessitates designing solutions that allow for performing secure computation on a multilayer graph which is distributed among mutually distrusting parties while keeping each party’s data hidden. The work of Asharov et al. (WWW’17) addresses this problem by designing secure solutions for centrality measures that involve computing the truncated Katz score and reach score on multilayer graphs. However, we identify several limitations in that work which render the solution inefficient or even unfeasible for realistic networks with significantly more than 10k nodes. We address these limitations by designing secure solutions that are significantly more efficient and scalable. In more detail, given that real-world graphs are known to be sparse, our solutions move away from an expensive matrix-based representation to a more efficient list-based representation. We design novel, secure, and efficient solutions for computing centrality measures and prove their correctness. Our solutions drastically reduce the asymptotic complexity from the prohibitive 𝒪(|V|²) even for the fastest solution by Asharov et al. down to 𝒪(|V|log|V|), for |V| nodes. To design our solutions, we extend upon the secure graph computation framework of Koti et al. (CCS’24), providing a novel framework with improved capabilities in multiple directions. Finally, we provide an end-to-end implementation of our secure graph analysis framework and establish concrete efficiency improvements over prior work, observing several orders of magnitude improvement. Image showing part 2 of abstract. Image showing part 3 of abstract.
June 5, 2025 at 7:59 AM
thoschneider.bsky.social
In this upcoming #ASIACCS25 paper, we implement an MPC framework in the memory-safe language Rust. Our focus is on scalability & optimizing memory footprint for secret-sharing-based MPC protocols while maintaining low round complexity. #ENCRYPTO_Group @cs-tudarmstadt.bsky.social @tuda.bsky.social
eprint.ing.bot ePrint Updates @eprint.ing.bot · May 23
SEEC: Memory Safety Meets Efficiency in Secure Two-Party Computation (Henri Dohmen, Robin Hundt, Nora Khayata, Thomas Schneider) ia.cr/2025/930
Abstract. Secure Multi-Party Computation (MPC) allows multiple parties to perform privacy-preserving computation on their secret data. MPC protocols based on secret sharing have high throughput which makes them well-suited for batch processing, where multiple instances are evaluated in parallel. So far, practical implementations of secret sharing-based MPC protocols mainly focus on runtime and communication efficiency, so the memory overhead of protocol implementations is often overlooked. Established techniques to reduce the memory overhead for constant-round garbled circuit protocols cannot be directly applied to secret sharing-based protocols because they would increase the round complexity. Additionally, state-of-the-art implementations of secret sharing-based MPC protocols are implemented in C/C++ and may exhibit memory unsafety and memory leaks, which could lead to undefined behavior. In this paper, we present SEEC: SEEC Executes Enormous Circuits, a framework for secret sharing-based MPC with a novel approach to address memory efficiency and safety without compromising on runtime and communication efficiency. We realize SEEC in Rust, a language known for memory-safety at close-to-native speed. To reduce the memory footprint, we develop an in-memory representation for sub-circuits. Thus, we never inline sub-circuit calls during circuit evaluation, a common issue that blows up memory usage in MPC implementations. We compare SEEC with the state-of-the-art secret sharing-based MPC frameworks ABY (NDSS’15), MP-SPDZ (CCS’20), and MOTION (TOPS’22) w.r.t. runtime, memory, and communication efficiency. Our results show that our reliable and memory-safe implementation has competitive or even better performance. Image showing part 2 of abstract.
May 23, 2025 at 8:36 AM