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Machine learning is nowadays supplanting or extending human expertise in many domains ranging from board games to text translation. Correspondingly, the use of such tools is also on the rise in comput...
Physical Unclonable Functions (PUFs) and, in particular, XOR Arbiter PUFs have gained much research interest as an authentication mechanism for embedded systems. One of the biggest problems of (strong...
At CRYPTO 2018 Cramer et al. presented SPDZ2k, a new secret-sharing based protocol for actively secure multi-party computation against a dishonest majority, that works over rings instead of fields. Th...
Machine learning techniques represent a powerful option in profiling side-channel analysis. Still, there are many settings where their performance is far from expected. In such occasions, it is very i...
Profiling attacks, especially those based on machine learning proved as very successful techniques in recent years when considering side-channel analysis of block ciphers implementations. At the same ...
Exploiting data sparsity is crucial for the scalability of many data analysis tasks. However, while there is an increasing interest in efficient secure computation protocols for distributed machine le...
How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem. CodedPrivateML keeps both the data an...
In the era of lightweight cryptography, designing cryptographically good and power efficient 4x4 S-boxes is a challenging problem. While the optimal cryptographic properties are easy to determine, ver...
Logic locking has been proposed as a strong protection of intellectual property (IP) against security threats in the IC supply chain especially when the fabrication facility is untrusted. Various tech...
Homomorphic encryption has the purpose to allow computations on encrypted data, without the need for decryption other than that of the final result. This could provide an elegant solution to the probl...
We concentrate on machine learning techniques used for profiled side-channel analysis when having imbalanced data. Such scenarios are realistic and often occurring, for instance in the Hamming weight ...
In the context of Fully Homomorphic Encryption, which allows computations on encrypted data, Machine Learning has been one of the most popular applications in the recent past. All of these works, howe...
Machine learning is widely used to produce models for a range of applications and is increasingly offered as a service by major technology companies. However, the required massive data collection rais...
Silicon Physically Unclonable Functions (PUFs) have been proposed as an emerging hardware security primitive in various applications such as device identification, authentication and cryptographic key...
Machine learning algorithms are used by more and more online applications to improve the services. Machine learning-based online services are usually accessed by thousands of clients concurrently thro...

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