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1. Toward Easier Development of Privacy-Preserving Mobile Crowdsensing Applications

   https://doi.org/10.1109/MDM65600.2025.00058  

   Murray, Jeffrey; Lagesse, Brent (June 2025, IEEE)

   Fully Homomorphic Encryption (FHE) schemes allow computations over encrypted data without access to the decryption key. This technique can be a valuable tool for building privacy into crowdsensing systems; however, many existing FHE implementations, such as Microsoft's SEAL, are difficult to implement into mobile applications. This paper presents a natively compiled Dart plugin that abstracts the underlying C/C++ SEAL library. The FHE Library plugin enables developers to access SEAL's full functionality within other Dart plugins and Flutter applications and is extensible to other encryption libraries. To evaluate the versatility of the plugin, we develop a Dart plugin to calculate several distance measures between two sets of encrypted inputs and we develop a Flutter application called GhostPeerShare. The Distance Measure plugin implements Kullback-Leibler Divergence, Bhattacharyya Coefficient, and Cramer Distance. GhostPeerShare demonstrates the use of a plugin by re-implementing Proof of Presence Share (PopShare), a mobile application that privately identifies similar videos recorded by users, as a Flutter application. Through these applications, we demonstrate that performance is similar to native applications and that utilizing FHE is more accessible to researchers developing crowdsensing applications. 

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2. Automatic Security Bug Detection with FindSecurityBugs Plugin

   Shahriar, Hossain; Riad, Kmarul; Talukder, Arabin; Zhang, Hao; Li, Zhuolin (October 2019, Conference on Cybersecurity Education, Research and Practice)

   The security threats to mobile application are growing explosively. Mobile app flaws and security defects could open doors for hackers to easily attack mobile apps. Secure software development must be addressed earlier in the development life cycle rather than fixing the security holes after attacking. Early eliminating against possible security vulnerability will help us increase the security of software and mitigate the consequence of damages of data loss caused by potential malicious attacking. In this paper, we present a static security analysis approach with open source FindSecurityBugs plugin for Android Studio IDE. We demonstrate that integration of the plugin enables developers secure mobile application and mitigating security risks during implementation time in Android Studio IDE. We demonstrate that integration of the plugin enables developers secure mobile application and mitigating security risks during implementation time. Secure software development must be addressed earlier in the development lifecycle rather than fixing the security holes after attacking. Early eliminating against possible security vulnerability will help us increase the security of software and mitigate the consequence of damages of data loss caused by potential malicious attacking. In this paper, we present a static security analysis approach with open source FindSecurityBugs plugin for Android Studio IDE. We demonstrate that integration of the plugin enables developers secure mobile application and mitigating security risks during implementation time. 

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3. Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients

   Moses, William; Churavy, Valentin (January 2020, Advances in neural information processing systems)

   Applying differentiable programming techniques and machine learning algorithms to foreign programs requires developers to either rewrite their code in a machine learning framework, or otherwise provide derivatives of the foreign code. This paper presents Enzyme, a high-performance automatic differentiation (AD) compiler plugin for the LLVM compiler framework capable of synthesizing gradients of statically analyzable programs expressed in the LLVM intermediate representation (IR). Enzyme synthesizes gradients for programs written in any language whose compiler targets LLVM IR including C, C++, Fortran, Julia, Rust, Swift, MLIR, etc., thereby providing native AD capabilities in these languages. Unlike traditional source-to-source and operator-overloading tools, Enzyme performs AD on optimized IR. On a machine-learning focused benchmark suite including Microsoft's ADBench, AD on optimized IR achieves a geometric mean speedup of 4.2 times over AD on IR before optimization allowing Enzyme to achieve state-of-the-art performance. Packaging Enzyme for PyTorch and TensorFlow provides convenient access to gradients of foreign code with state-of-the-art performance, enabling foreign code to be directly incorporated into existing machine learning workflows. 

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4. Understanding Container Network Interface Plugins: Design Considerations and Performance

   https://doi.org/10.1109/LANMAN49260.2020.9153266  

   Qi, Shixiong; Kulkarni, Sameer G; Ramakrishnan, K. K. (July 2020, 26th {IEEE} International Symposium on Local and Metropolitan Area Networks, {LANMAN} 2020, Orlando, FL, USA, July 13-15, 2020)

   Kubernetes, an open-source container orchestration platform, has been widely adopted by cloud service providers (CSPs) for its advantages in simplifying container deployment, scalability and scheduling. Networking is one of the central components of Kubernetes, providing connectivity between different pods (group of containers) both within the same host and across hosts. To bootstrap Kubernetes networking, the Container Network Interface (CNI) provides a unified interface for the interaction between container runtimes. There are several CNI implementations, available as open-source ‘CNI plugins’. While they differ in functionality and performance, it is a challenge for a cloud provider to differentiate and choose the appropriate plugin for their environment. In this paper, we compare the various open source CNI plugins available from the community, qualitatively and through detailed quantitative measurements. With our experimental evaluation, we analyze the overheads and bottlenecks for each CNI plugin, as a result of the network model it implements, interaction with the host network protocol stack and the network policies implemented in iptables rules. The choice of the CNI plugin may also be based on whether intra-host or inter-host communication dominates. 

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5. PISA-SPARKY: an interactive SPARKY plugin to analyze oriented solid-state NMR spectra of helical membrane proteins

   https://doi.org/10.1093/bioinformatics/btaa019  

   Weber, Daniel K; Wang, Songlin; Markley, John L; Veglia, Gianluigi; Lee, Woonghee; Elofsson, Arne (January 2020, Bioinformatics)

   Abstract Motivation Two-dimensional [15N-1H] separated local field solid-state nuclear magnetic resonance (NMR) experiments of membrane proteins aligned in lipid bilayers provide tilt and rotation angles for α-helical segments using Polar Index Slant Angle (PISA)-wheel models. No integrated software has been made available for data analysis and visualization. Results We have developed the PISA-SPARKY plugin to seamlessly integrate PISA-wheel modeling into the NMRFAM-SPARKY platform. The plugin performs basic simulations, exhaustive fitting against experimental spectra, error analysis and dipolar and chemical shift wave plotting. The plugin also supports PyMOL integration and handling of parameters that describe variable alignment and dynamic scaling encountered with magnetically aligned media, ensuring optimal fitting and generation of restraints for structure calculation. Availability and implementation PISA-SPARKY is freely available in the latest version of NMRFAM-SPARKY from the National Magnetic Resonance Facility at Madison (http://pine.nmrfam.wisc.edu/download_packages.html), the NMRbox Project (https://nmrbox.org) and to subscribers of the SBGrid (https://sbgrid.org). The pisa.py script is available and documented on GitHub (https://github.com/weberdak/pisa.py) along with a tutorial video and sample data. Supplementary information Supplementary data are available at Bioinformatics online. 

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