搜索结果: 1-15 共查到“军事学 Machine learning”相关记录25条 . 查询时间(0.093 秒)
Machine learning and side channel analysis in a CTF competition
key recovery deep learning machine learning
2019/7/25
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...
Breaking the Lightweight Secure PUF: Understanding the Relation of Input Transformations and Machine Learning Resistance
Physically Unclonable Function Machine Learning Modelling Attack
2019/7/15
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...
New Primitives for Actively-Secure MPC over Rings with Applications to Private Machine Learning
MPC Decision Trees SVM
2019/6/3
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...
Bias-variance Decomposition in Machine Learning-based Side-channel Analysis
Side-channel analysis Machine learning Deep learning
2019/5/28
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...
One trace is all it takes: Machine Learning-based Side-channel Attack on EdDSA
Side-channel attacks EdDSA Machine learning
2019/4/10
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 ...
Make Some ROOM for the Zeros: Data Sparsity in Secure Distributed Machine Learning
secure computation machine learning
2019/3/13
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...
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
privacy-preserving machine learning information-theoretic privacy
2019/2/26
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...
Modeling Power Efficiency of S-boxes Using Machine Learning
Power Efficiency Optimal S-box Dynamic power
2019/2/26
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...
Secure and Effective Logic Locking for Machine Learning Applications
Logic Locking SAT Attack Machine Learning Applications
2019/1/9
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...
Conditionals in Homomorphic Encryption and Machine Learning Applications
Homomorphic encryption conditionals clashes with fundamental encryption requirements
2018/11/2
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...
The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations
Profiled side-channel attacks Imbalanced datasets Synthetic examples
2018/5/28
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 ...
Unsupervised Machine Learning on Encrypted Data
Machine Learning Clustering Fully Homomorphic Encryption
2018/5/11
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...
The Interpose PUF: Secure PUF Design against State-of-the-art Machine Learning Attacks
majority voting modeling attack propagation criterion
2018/4/20
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...
Faster Multiplication Triplet Generation from Homomorphic Encryption for Practical Privacy-Preserving Machine Learning under a Narrow Bandwidth
Privacy-preserving Machine Learning Secure Two-party Computation Applied Crypto
2018/2/8
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...