A review of privacy-preserving techniques for deep learning
Deep learning is one of the advanced approaches of machine learning, and has attracted a
growing attention in the recent years. It is used nowadays in different domains and …
growing attention in the recent years. It is used nowadays in different domains and …
Privacy attacks against deep learning models and their countermeasures
Recently, deep learning is considered an important concept that is used in a lot of important
applications, which require accurate models, such as image classification, identification of …
applications, which require accurate models, such as image classification, identification of …
On sharing models instead of data using mimic learning for smart health applications
Electronic health records (EHR) systems contain vast amounts of medical information about
patients. These data can be used to train machine learning models that can predict health …
patients. These data can be used to train machine learning models that can predict health …
Mimic learning to generate a shareable network intrusion detection model
Purveyors of malicious network attacks continue to increase the complexity and the
sophistication of their techniques, and their ability to evade detection continues to improve …
sophistication of their techniques, and their ability to evade detection continues to improve …
Privacy-preserving fair item ranking
Users worldwide access massive amounts of curated data in the form of rankings on a daily
basis. The societal impact of this ease of access has been studied and work has been done …
basis. The societal impact of this ease of access has been studied and work has been done …
Making complex prediction rules applicable for readers: current practice in random forest literature and recommendations
AL Boulesteix, S Janitza, R Hornung… - Biometrical …, 2019 - Wiley Online Library
Ideally, prediction rules should be published in such a way that readers may apply them, for
example, to make predictions for their own data. While this is straightforward for simple …
example, to make predictions for their own data. While this is straightforward for simple …
Report on the second SIGIR workshop on neural information retrieval (Neu-IR'17)
The second SIGIR workshop on neural information retrieval (Neu-IR? 17) took place on
August 11, 2017, in Tokyo, Japan. Following the successful 2016 edition, the workshop …
August 11, 2017, in Tokyo, Japan. Following the successful 2016 edition, the workshop …
Privacy of Deep Learning Systems: A Penetration Testing Framework
BJ Vayghan - Authorea Preprints, 2024 - techrxiv.org
The advent of deep learning has revolutionized various data-driven fields such as image
recognition, natural language processing, and autonomous vehicles. Despite its …
recognition, natural language processing, and autonomous vehicles. Despite its …
Towards privacy-preserving and fairness-enhanced item ranking in recommender systems
JA Sun - 2023 - papyrus.bib.umontreal.ca
We present a novel privacy-preserving approach to enhance item fairness in ranking
systems. We employ post-processing techniques in a multi-stakeholder recommendation …
systems. We employ post-processing techniques in a multi-stakeholder recommendation …
Towards theoretical understanding of weak supervision for information retrieval
Neural network approaches have recently shown to be effective in several information
retrieval (IR) tasks. However, neural approaches often require large volumes of training data …
retrieval (IR) tasks. However, neural approaches often require large volumes of training data …