29 citations · 77 across the 10 of their papers we have counts for
10 papers
Differentially Private Heavy Hitter Detection using Federated Analytics
Karan Chadha, Junye Chen, John Duchi +5
In this work, we study practical heuristics to improve the performance of prefix-tree based algorithms for differentially private heavy hitter detection. Our model assumes each use…
Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems
Hanieh Hashemi, Wenjie Xiong, Liu Ke +4
Online personalized recommendation services are generally hosted in the cloud where users query the cloud-based model to receive recommended input such as merchandise of interest o…
DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware
Hanieh Hashemi, Yongqin Wang, Murali Annavaram
Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train or infer with private data while exploitin…
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings
Tiantian Feng, Hanieh Hashemi, Rajat Hebbar +2
Speech emotion recognition (SER) processes speech signals to detect and characterize expressed perceived emotions. Many SER application systems often acquire and transmit speech da…
Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning
Tingting Tang, Ramy E. Ali, Hanieh Hashemi +3
Stragglers, Byzantine workers, and data privacy are the main bottlenecks in distributed cloud computing. Some prior works proposed coded computing strategies to jointly address all…
Byzantine-Robust and Privacy-Preserving Framework for FedML
Hanieh Hashemi, Yongqin Wang, Chuan Guo +1
Federated learning has emerged as a popular paradigm for collaboratively training a model from data distributed among a set of clients. This learning setting presents, among others…
Privacy and Integrity Preserving Training Using Trusted Hardware
Hanieh Hashemi, Yongqin Wang, Murali Annavaram
Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train with private data while exploiting acceler…
Secure and Fault Tolerant Decentralized Learning
Saurav Prakash, Hanieh Hashemi, Yongqin Wang +2
Federated learning (FL) is a promising paradigm for training a global model over data distributed across multiple data owners without centralizing clients' raw data. However, shari…
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks
Hanieh Hashemi, Yongqin Wang, Murali Annavaram
Protecting the privacy of input data is of growing importance as machine learning methods reach new application domains. In this paper, we provide a unified training and inference…
PartitionedVC: Partitioned External Memory Graph Analytics Framework for SSDs
Kiran Kumar Matam, Hanieh Hashemi, Murali Annavaram
Graph analytics are at the heart of a broad range of applications such as drug discovery, page ranking, and recommendation systems. When graph size exceeds memory size, out-of-core…