Secure multiparty computation and trusted hardware: Examining adoption challenges and opportunities
JI Choi, KRB Butler - Security and Communication Networks, 2019 - Wiley Online Library
When two or more parties need to compute a common result while safeguarding their
sensitive inputs, they use secure multiparty computation (SMC) techniques such as garbled …
sensitive inputs, they use secure multiparty computation (SMC) techniques such as garbled …
: High-Dimensional Crowdsourced Data Publication With Local Differential Privacy
High-dimensional crowdsourced data collected from numerous users produces rich
knowledge about our society; however, it also brings unprecedented privacy threats to the …
knowledge about our society; however, it also brings unprecedented privacy threats to the …
Distributed learning without distress: Privacy-preserving empirical risk minimization
Distributed learning allows a group of independent data owners to collaboratively learn a
model over their data sets without exposing their private data. We present a distributed …
model over their data sets without exposing their private data. We present a distributed …
Secure data aggregation of lightweight E-healthcare IoT devices with fair incentives
W Tang, J Ren, K Deng, Y Zhang - IEEE Internet of Things …, 2019 - ieeexplore.ieee.org
With rapid development of e-healthcare systems, patients that are equipped with resource-
limited e-healthcare devices (Internet of Things) generate huge amount of health data for …
limited e-healthcare devices (Internet of Things) generate huge amount of health data for …
A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions
The rapid advances in the Internet of Things (IoT) have promoted a revolution in
communication technology and offered various customer services. Artificial intelligence (AI) …
communication technology and offered various customer services. Artificial intelligence (AI) …
Privacy-preserving data aggregation against malicious data mining attack for IoT-enabled smart grid
Internet of Things (IoT)-enabled smart grids can achieve more reliable and high-frequency
data collection and transmission compared with existing grids. However, this frequent data …
data collection and transmission compared with existing grids. However, this frequent data …
Blockflow: An accountable and privacy-preserving solution for federated learning
Federated learning enables the development of a machine learning model among
collaborating agents without requiring them to share their underlying data. However …
collaborating agents without requiring them to share their underlying data. However …
Honeycrisp: large-scale differentially private aggregation without a trusted core
Recently, a number of systems have been deployed that gather sensitive statistics from user
devices while giving differential privacy guarantees. One prominent example is the …
devices while giving differential privacy guarantees. One prominent example is the …
[PDF][PDF] Smpai: Secure multi-party computation for federated learning
Federated Learning is a technique that enables a large number of users to jointly learn a
shared machine learning model, managed by a centralized server, while the training data …
shared machine learning model, managed by a centralized server, while the training data …
Fairness and privacy preserving in federated learning: A survey
Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …
addresses privacy concerns by allowing participants to collaboratively train machine …