Cloud Data Security Solutions

Vertical Federated Learning: Concepts, Advances and Challenges

Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models without exposing...

ARPaCCino: An Agentic-RAG for Policy as Code Compliance

Policy as Code (PaC) is a paradigm that encodes security and compliance policies into machine-readable formats, enabling automated enforcement in Infrastructure as Code (IaC) environments....

Graph Neural Network Based Adaptive Threat Detection for Cloud Identity and Access Management Logs

The rapid expansion of cloud infrastructures and distributed identity systems has significantly increased the complexity and attack surface of modern enterprises. Traditional rule based or...

Federated Learning with Differential Privacy: An Utility-Enhanced Approach

Federated learning has emerged as an attractive approach to protect data privacy by eliminating the need for sharing clients' data while reducing communication costs compared with centralized...

Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?

For small privacy parameter $\epsilon$, $\epsilon$-differential privacy (DP) provides a strong worst-case guarantee that no membership inference attack (MIA) can succeed at determining whether a...

GraphGuard: Detecting and Counteracting Training Data Misuse in Graph Neural Networks

The emergence of Graph Neural Networks (GNNs) in graph data analysis and their deployment on Machine Learning as a Service platforms have raised critical concerns about data misuse during model...

On-demand Container Loading in AWS Lambda

AWS Lambda is a serverless event-driven compute service, part of a category of cloud compute offerings sometimes called Function-as-a-service (FaaS). When we first released AWS Lambda, functions...

Tight Auditing of Differentially Private Machine Learning

Auditing mechanisms for differential privacy use probabilistic means to empirically estimate the privacy level of an algorithm. For private machine learning, existing auditing mechanisms are...

Privacy and Security Implications of Cloud-Based AI Services : A Survey

This paper details the privacy and security landscape in today's cloud ecosystem and identifies that there is a gap in addressing the risks introduced by machine learning models. As machine...

pvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing

This paper proposes a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing, enabling a CNN model developer to convince a user of the truthful CNN...

Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model

Federated Learning, as a popular paradigm for collaborative training, is vulnerable against privacy attacks. Different privacy levels regarding users' attitudes need to be satisfied locally, while...

Stateful Switch: Optimized Time Series Release with Local Differential Privacy

Time series data have numerous applications in big data analytics. However, they often cause privacy issues when collected from individuals. To address this problem, most existing works perturb...

REMISVFU: Vertical Federated Unlearning via Representation Misdirection for Intermediate Output Feature

Data-protection regulations such as the GDPR grant every participant in a federated system a right to be forgotten. Federated unlearning has therefore emerged as a research frontier, aiming to...

Privacy-Preserving Federated Learning from Partial Decryption Verifiable Threshold Multi-Client Functional Encryption

In federated learning, multiple parties can cooperate to train the model without directly exchanging their own private data, but the gradient leakage problem still threatens the privacy security...

Securing Generative AI in Healthcare: A Zero-Trust Architecture Powered by Confidential Computing on Google Cloud

The integration of Generative Artificial Intelligence (GenAI) in healthcare is impeded by significant security challenges unaddressed by traditional frameworks, precisely the data-in-use gap where...

Towards Federated Domain Unlearning: Verification Methodologies and Challenges

Federated Learning (FL) has evolved as a powerful tool for collaborative model training across multiple entities, ensuring data privacy in sensitive sectors such as healthcare and finance....

Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors

Federated machine learning leverages edge computing to develop models from network user data, but privacy in federated learning remains a major challenge. Techniques using differential privacy...

Tight Bounds for Machine Unlearning via Differential Privacy

We consider the formulation of "machine unlearning" of Sekhari, Acharya, Kamath, and Suresh (NeurIPS 2021), which formalizes the so-called "right to be forgotten" by requiring that a trained...