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Privacy concerns and protection behavior during the Covid-19 pandemic [PDF]
Problems and Perspectives in Management, 2022 This paper aims to analyze the protection behavior of employees while working remotely during the Covid-19 pandemic using online video chat software.
Ranjany Sundaram, Snehal Shetty
doaj +1 more source
APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning [PDF]
IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, 2022 Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in clas-sical machine learning.
Minseok Ryu +3 more
semanticscholar +1 more source
Embedding Privacy Into Design Through Software Developers: Challenges and Solutions [PDF]
IEEE Security and Privacy, 2022 To make privacy a first-class citizen in software, we argue for equipping developers with usable tools as well as providing support from organizations, educators, and regulators.
Mohammad Tahaei, Kami Vaniea, A. Rashid
semanticscholar +1 more source
"We are a startup to the core": A qualitative interview study on the security and privacy development practices in Turkish software startups [PDF]
IEEE Symposium on Security and Privacy, 2022 Security and privacy are often neglected in software development, and rarely a priority for developers. This insight is commonly based on research conducted by researchers and on developer populations living and working in the United States, Europe, and ...
Dilara Keküllüoğlu, Y. Acar
semanticscholar +1 more source
Transactions on Cryptographic Hardware and Embedded Systems, 2023 In this paper we present the first formally verified implementations of Kyber and, to the best of our knowledge, the first such implementations of any post-quantum cryptosystem.
José Bacelar Almeida +11 more
doaj +1 more source
Scaling Exponents of Time Series Data: A Machine Learning Approach
Entropy, 2023 In this study, we present a novel approach to estimating the Hurst exponent of time series data using a variety of machine learning algorithms. The Hurst exponent is a crucial parameter in characterizing long-range dependence in time series, and ...
Sebastian Raubitzek +3 more
doaj +1 more source
Listening to bluetooth beacons for epidemic risk mitigation
Scientific Reports, 2022 The ongoing COVID-19 pandemic let to efforts to develop and deploy digital contact tracing systems to expedite contact tracing and risk notification. Unfortunately, the success of these systems has been limited, partly owing to poor interoperability with
Gilles Barthe +10 more
doaj +1 more source
Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, 2016 Privacy violations in online social networks (OSNs) often arise as a result of users sharing information with unintended audiences. One reason for this is that, although OSN capabilities for creating and managing social groups can make it easier to be selective about recipients of a given post, they do not provide enough guidance to the users to make ...
Calikli, Gul +8 more
openaire +2 more sources
Deep Learning with Differential Privacy [PDF]
Conference on Computer and Communications Security, 2016 Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains. Often, the training of models requires large, representative datasets, which may be crowdsourced and contain sensitive information.
Martín Abadi +6 more
semanticscholar +1 more source
Privacy Profiles and Amplification by Subsampling
The Journal of Privacy and Confidentiality, 2020 Differential privacy provides a robust quantifiable methodology to measure and control the privacy leakage of data analysis algorithms. A fundamental insight is that by forcing algorithms to be randomized, their privacy leakage can be characterized by ...
Borja Balle +2 more
doaj +1 more source