arXiv subjects

Christian Rechberger

Publications and source records attributed to Christian Rechberger.

4 recordsLinked to original sources

arXiv2024

Hiding Your Awful Online Choices Made More Efficient and Secure: A New Privacy-Aware Recommender System

Recommender systems are an integral part of online platforms that recommend new content to users with similar interests. However, they demand a considerable amount of user activity data where, if the data is not adequately protected, constitute a critical threat to the user privacy. Privacy-aware recommender systems enable protection of such sensitive user data while still maintaining a similar recommendation accuracy compared to the traditional non-private recommender systems. However, at present, the current privacy-aware recommender systems suffer from a significant trade-off between privacy and computational efficiency. For instance, it is well known that architectures that rely purely on cryptographic primitives offer the most robust privacy guarantees, however, they suffer from substantial computational and network overhead. Thus, it is crucial to improve this trade-off for better performance. This paper presents a novel privacy-aware recommender system that combines privacy-aware machine learning algorithms for practical scalability and efficiency with cryptographic primitives like Homomorphic Encryption and Multi-Party Computation - without assumptions like trusted-party or secure hardware - for solid privacy guarantees. Experiments on standard benchmark datasets show that our approach results in time and memory gains by three orders of magnitude compared to using cryptographic primitives in a standalone for constructing a privacy-aware recommender system. Furthermore, for the first time our method makes it feasible to compute private recommendations for datasets containing 100 million entries, even on memory-constrained low-power SOC (System on Chip) devices.

cs.CR↗

arXiv2022

A Signature-Based Gr\"obner Basis Algorithm with Tail-Reduced Reductors (M5GB)

Gr\"obner bases are an important tool in computational algebra and, especially in cryptography, often serve as a boilerplate for solving systems of polynomial equations. Research regarding (efficient) algorithms for computing Gr\"obner bases spans a large body of dedicated work that stretches over the last six decades. The pioneering work of Bruno Buchberger in 1965 can be considered as the blueprint for all subsequent Gr\"obner basis algorithms to date. Among the most efficient algorithms in this line of work are signature-based Gr\"obner basis algorithms, with the first of its kind published in the late 1990s by Jean-Charles Faug\`ere under the name F5. In addition to signature-based approaches, Rusydi Makarim and Marc Stevens investigated a different direction to efficiently compute Gr\"obner bases, which they published in 2017 with their algorithm M4GB. The ideas behind M4GB and signature-based approaches are conceptually orthogonal to each other because each approach addresses a different source of inefficiency in Buchberger's initial algorithm by different means. We amalgamate those orthogonal ideas and devise a new Gr\"obner basis algorithm, called M5GB, that combines the concepts of both worlds. In that capacity, M5GB merges strong signature-criteria to eliminate redundant S-pairs with concepts for fast polynomial reductions borrowed from M4GB. We provide proofs of termination and correctness and a proof-of-concept implementation in C++ by means of the Mathic library. The comparison with a state-of-the-art signature-based Gr\"obner basis algorithm (implemented via the same library) validates our expectations of an overall faster runtime for quadratic overdefined polynomial systems that have been used in comparisons before in the literature and are also part of cryptanalytic challenges.

math.AC↗

arXiv2022

CryptoTL: Private, Efficient and Secure Transfer Learning

Big data has been a pervasive catchphrase in recent years, but dealing with data scarcity has become a crucial question for many real-world deep learning (DL) applications. A popular methodology to efficiently enable the training of DL models to perform tasks in scenarios with low availability of data is transfer learning (TL). TL allows to transfer knowledge from a general domain to a specific target one. However, such a knowledge transfer may put privacy at risk when it comes to sensitive or private data. With CryptoTL we introduce a solution to this problem, and show for the first time a cryptographic privacy-preserving TL approach based on homomorphic encryption that is efficient and feasible for real-world use cases. We achieve this by carefully designing the framework such that training is always done in plain while still profiting from the privacy gained by homomorphic encryption. To demonstrate the efficiency of our framework, we instantiate it with the popular CKKS HE scheme and apply CryptoTL to classification tasks with small datasets and show the applicability of our approach for sentiment analysis and spam detection. Additionally, we highlight how our approach can be combined with differential privacy to further increase the security guarantees. Our extensive benchmarks show that using CryptoTL leads to high accuracy while still having practical fine-tuning and classification runtimes despite using homomorphic encryption. Concretely, one forward-pass through the encrypted layers of our setup takes roughly 1s on a notebook CPU.

cs.CR↗

arXiv2020

Privately Connecting Mobility to Infectious Diseases via Applied Cryptography

Recent work has shown that cell phone mobility data has the unique potential to create accurate models for human mobility and consequently the spread of infected diseases. While prior studies have exclusively relied on a mobile network operator's subscribers' aggregated data in modelling disease dynamics, it may be preferable to contemplate aggregated mobility data of infected individuals only. Clearly, naively linking mobile phone data with health records would violate privacy by either allowing to track mobility patterns of infected individuals, leak information on who is infected, or both. This work aims to develop a solution that reports the aggregated mobile phone location data of infected individuals while still maintaining compliance with privacy expectations. To achieve privacy, we use homomorphic encryption, validation techniques derived from zero-knowledge proofs, and differential privacy. Our protocol's open-source implementation can process eight million subscribers in 70 minutes.

cs.CR↗