Privacy-preserving machine learning: Methods, challenges and directions
Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …
domains. Usually, a well-performing ML model relies on a large volume of training data and …
Hybridalpha: An efficient approach for privacy-preserving federated learning
Federated learning has emerged as a promising approach for collaborative and privacy-
preserving learning. Participants in a federated learning process cooperatively train a model …
preserving learning. Participants in a federated learning process cooperatively train a model …
A review of functional encryption in IoT applications
The Internet of Things (IoT) represents a growing aspect of how entities, including humans
and organizations, are likely to connect with others in their public and private interactions …
and organizations, are likely to connect with others in their public and private interactions …
A privacy-preserving federated learning for multiparty data sharing in social IoTs
As 5G and mobile computing are growing rapidly, deep learning services in the Social
Computing and Social Internet of Things (IoT) have enriched our lives over the past few …
Computing and Social Internet of Things (IoT) have enriched our lives over the past few …
Sok: Secure aggregation based on cryptographic schemes for federated learning
Secure aggregation consists of computing the sum of data collected from multiple sources
without disclosing these individual inputs. Secure aggregation has been found useful for …
without disclosing these individual inputs. Secure aggregation has been found useful for …
Fedv: Privacy-preserving federated learning over vertically partitioned data
Federated learning (FL) has been proposed to allow collaborative training of machine
learning (ML) models among multiple parties to keep their data private and only model …
learning (ML) models among multiple parties to keep their data private and only model …
Boosting privately: Federated extreme gradient boosting for mobile crowdsensing
Recently, Google and other 24 institutions proposed a series of open challenges towards
federated learning (FL), which include application expansion and homomorphic encryption …
federated learning (FL), which include application expansion and homomorphic encryption …
Decentralized multi-client functional encryption for inner product
We consider a situation where multiple parties, owning data that have to be frequently
updated, agree to share weighted sums of these data with some aggregator, but where they …
updated, agree to share weighted sums of these data with some aggregator, but where they …
Efficient privacy-preserving electricity theft detection with dynamic billing and load monitoring for AMI networks
In advanced metering infrastructure (AMI), smart meters (SMs) are installed at the consumer
side to send fine-grained power consumption readings periodically to the system operator …
side to send fine-grained power consumption readings periodically to the system operator …
[HTML][HTML] Preserving data privacy in machine learning systems
The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …
the need to collect and process large volumes of data, some of which are considered …