CCS

With the rapid development of technology, user privacy and data security are drawing much attention over the recent years. On one hand, how to protect user privacy while making use of customers? data is a challenging task. On the other hand, data silos ...

Privacy-preserving machine learning (PPML) protocols allow to privately evaluate or even train machine learning (ML) models on sensitive data while simultaneously protecting the data and the model. So far, most of these protocols were built and ...

Multiple organizations often wish to aggregate their sensitive data and learn from it, but they cannot do so because they cannot share their data. For example, banks wish to train models jointly over their aggregate transaction data to detect money ...

Machine learning has become increasingly prominent and is widely used in various applications in practice. Despite its great success, the integrity of machine learning predictions and accuracy is a rising concern. The reproducibility of machine learning ...

The ubiquitous deployment of machine learning (ML) technologies has certainly improved many applications but also raised challenging privacy concerns, as sensitive client data is usually processed remotely at the discretion of a service provider. ...

Deployment of deep learning in different fields and industries is growing day by day due to its performance, which relies on the availability of data and compute. Data is often crowd-sourced and contains sensitive information about its contributors, ...

In recent years, gradient boosted decision tree learning has proven to be an effective method of training robust models. Moreover, collaborative learning among multiple parties has the potential to greatly benefit all parties involved, but organizations ...

Many companies provide neural network prediction services to users for a wide range of applications. However, current prediction systems compromise one party's privacy: either the user has to send sensitive inputs to the service provider for ...

Federated learning aggregates data from multiple sources while protecting privacy, which makes it possible to train efficient models in real scenes. However, although federated learning uses encrypted security aggregation, its decentralised nature makes ...

Graph Neural Networks (GNNs) has achieved tremendous development on perceptual tasks in recent years, such as node classification, graph classification, link prediction, etc. However, recent studies show that deep learning models of GNNs are incredibly ...

We present an extended abstract of MP2ML, a machine learning framework which integrates Intel nGraph-HE, a homomorphic encryption (HE) framework, and the secure two-party computation framework ABY, to enable data scientists to perform private inference ...

Most of the secure multi-party computation (MPC) machine learning methods can only afford simple gradient descent (sGD 1) optimizers, and are unable to benefit from the recent progress of adaptive GD optimizers (e.g., Adagrad, Adam and their variants), ...

Currently, financial institutions utilize personal sensitive information extensively in machine learning. It results in significant privacy risks to customers. As an essential standard of privacy, differential privacy is often applied to machine ...

This work provides a comprehensive review of existing frameworks based on secure computing techniques in the context of private image classification. The in-depth analysis of these approaches is followed by careful examination of their performance costs,...

The membership inference attack refers to the attacker's purpose to infer whether the data sample is in the target classifier training dataset. The ability of an adversary to ascertain the presence of an individual constitutes an obvious privacy threat ...

We present TinyGarble2 -- a C++ framework for privacy-preserving computation through the Yao's Garbled Circuit (GC) protocol in both the honest-but-curious and the malicious security models. TinyGarble2 provides a rich library with arithmetic and logic ...