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Decision tree and its generalization of random forests are a simple yet powerful machine learning model for many classification and regression problems. Recent works propose how to privately evaluate ...
Location data is an important piece of contextual information in location-driven features for geosocial and pervasive computing applications. In this paper, we propose to geo-hash locations using spac...
An established ingredient in the security evaluation of cryptographic devices is leakage detection, whereby physically observable characteristics such as the power consumption are measured during oper...
Convolutional neural networks (CNNs) is a category of deep neural networks that are primarily used for classifying image data. Yet, their continuous gain in popularity poses important privacy concerns...
Code-based cryptographic schemes recently raised to prominence as quantum-safe alternatives to the currently employed number-theoretic constructions, which do not resist quantum attacks. In this artic...
Deep learning is becoming a basis of decision making systems in many application domains, such as autonomous vehicles, health systems, etc., where the risk of misclassification can lead to serious con...
Although security against side-channel attacks is not an explicit design criterion of the NIST post-quantum standardization effort, it is certainly a major concern for schemes that are meant for real-...
A decision graph is a well-studied classifier and has been used to solve many real-world problems. We assumed a typical scenario between two parties in this study, in which one holds a decision graph ...
We propose a general method for security evaluation of SNOW 2.0-like ciphers against correlation attacks that are built similarly to known attacks on SNOW 2.0. Unlike previously known methods, the met...
In this paper, a platform named PEIGEN is presented to evaluate security, find efficient software/hardware implementations, and generate cryptographic S-boxes. Continuously developed for decades, S-bo...
Machine Learning models, and specially convolutional neural networks (CNNs), are at the heart of many day-to-day applications like image classification and speech recognition. The need for evaluating ...
Experts forecast that quantum computers can break classical cryptographic algorithms. Scientists are developing post quantum cryptographic (PQC) algorithms, that are invulnerable to quantum computer a...
Dodis and Yu (TCC 2013) studied how the security of cryptographic primitives that are secure in the "ideal" model in which the distribution of a randomness is the uniform distribution, is degraded whe...
In recent years, Mixed Integer Linear Programming (MILP) has been widely used in cryptanalysis of symmetric-key primitives. For differential and linear cryptanalysis, MILP can be used to solve the two...
Oblivious linear evaluation (OLE) is a two party protocol that allows a receiver to compute an evaluation of a sender's private, degree 11 polynomial, without letting the sender learn the evaluation p...

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