Due to high capacity and fast transmission speed, 5G plays a key role in modern electronic
infrastructure. Meanwhile, sparse tensor factorization (STF) is a useful tool for dimension …

We survey distributed deep learning models for training or inference without accessing raw
data from clients. These methods aim to protect confidential patterns in data while still …

Big data analysis has become a crucial part of new emerging technologies such as the
internet of things, cyber-physical analysis, deep learning, anomaly detection, etc. Among …

We study the problem of maintaining a differentially private decaying sum under continual
observation. We give a unifying framework and an efficient algorithm for this problem for any …

The covariance matrix of a dataset is a fundamental statistic that can be used for calculating
optimum regression weights as well as in many other learning and data analysis settings …

Randomized algorithms are efficient techniques for big data tensor analysis. In this tutorial
paper, we review and extend a variety of randomized algorithms for decomposing large …

We perform a rigorous study of private matrix analysis when only the last $ W $ updates to
matrices are considered useful for analysis. We show the existing framework in the non …

We consider the problem of approximating a $ d\times d $ covariance matrix $ M $ with a
rank-$ k $ matrix under $(\varepsilon,\delta) $-differential privacy. We present and analyze a …

In this paper, we present two new algorithms for covariance estimation under concentrated
differential privacy (zCDP). The first algorithm achieves a Frobenius error of $\tilde …

We present universal estimators for the statistical mean, variance, and scale (in particular,
the interquartile range) under pure differential privacy. These estimators are universal in the …