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Found 8 papers, 2 papers with code
Local Differential Privacy for Smart Meter Data Sharing
In this paper, we propose a novel LDP approach (named LDP-SmartEnergy) that utilizes randomized response techniques with sliding windows to facilitate the sharing of appliance-level energy consumption data over time while not revealing individual users' appliance usage patterns.
Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches
Biometric data, such as face images, are often associated with sensitive information (e. g medical, financial, personal government records).
Advancements of federated learning towards privacy preservation: from federated learning to split learning
In practical scenarios, all clients do not have sufficient computing resources (e. g., Internet of Things), the machine learning model has millions of parameters, and its privacy between the server and the clients while training/testing is a prime concern (e. g., rival parties).
Privacy Preserving Face Recognition Utilizing Differential Privacy
Biometric information delivered to untrusted third-party servers in an uncontrolled manner can be considered a significant privacy leak (i. e. uncontrolled information release) as biometrics can be correlated with sensitive data such as healthcare or financial records.
Cryptography and Security Databases
SplitFed: When Federated Learning Meets Split Learning
SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server.
Privacy Preserving Distributed Machine Learning with Federated Learning
Distributed devices such as the Internet of Things (IoT) often produce a large amount of data, eventually resulting in big data that can be vital in uncovering hidden patterns, and other insights in numerous fields such as healthcare, banking, and policing.
Databases
Local Differential Privacy for Deep Learning
However, DL algorithms tend to leak privacy when trained on highly sensitive crowd-sourced data such as medical data.
A Fuzzy Based Model to Identify Printed Sinhala Characters (ICIAfS14)
This unique feature makes it a challenge to extend the prevailing techniques to improve recognition of Sinhala characters.
