More Like this

1. Data Security and Privacy for Outsourced Data in the Cloud

   Sahin, Cetin; El Abbadi, Amr (April 2018, 34th IEEE International Conference on Data Engineering, ICDE 2018)

   Although outsourcing data to cloud storage has become popular, the increasing concerns about data security and privacy in the cloud limits broader cloud adoption. Ensuring data security and privacy, therefore, is crucial for better and broader adoption of the cloud. This tutorial provides a comprehensive analysis of the state-of-the-art in the context of data security and privacy for outsourced data. We aim to cover common security and privacy threats for outsourced data, and relevant novel schemes and techniques with their design choices regarding security, privacy, functionality, and performance. Our explicit focus is on recent schemes from both the database and the cryptography and security communities that enable query processing over encrypted data and access oblivious cloud storage systems. 

   more » « less
2. Enabling data science for the majority

   https://doi.org/10.14778/3352063.3352148  

   Parameswaran, Aditya (August 2019, Proceedings of the VLDB Endowment)
3. Training Data for the NeonTreeEvaluation Benchmark

   https://doi.org/10.5281/zenodo.3459803  

   Weinstein, Ben; Marconi, Sergio; White, Ethan (January 2020, Zenodo)

   This dataset is the large training data files for the NeonTreeEvaluation Benchmark for individual tree detection from airborne imagery. For each geographic site, given by the NEON four letter code (e.g HARV -> Harvard Forest), there are three files: a RGB image, a LiDAR tile, and a 426 band hyperpspectral file. For more information on the benchmark, and the corresponding R package, see https://github.com/weecology/NeonTreeEvaluation_package</p> 

   more » « less
4. The Data Analytics Framework for XDMoD

   https://doi.org/10.1007/s42979-024-02789-2  

   Weeden, Aaron; White, Joseph P; DeLeon, Robert L; Rathsam, Ryan; Simakov, Nikolay A; Saeli, Conner; Furlani, Thomas R (June 2024, SN Computer Science)
5. Socioeconomic Data for the Permian Basin

   Ross, E (May 2024, National Renewable Energy Laboratory)

   This dataset was compiled to support socioeconomic analyses of counties in the Permian Basin in Texas and New Mexico for the Permian Energy Development Lab (PEDL). The metrics in the dataset are organized into four categories: Socioeconomics, Health & Wellbeing, Jobs & Workforce, and Energy Infrastructure & Potential. The metrics originated from previously published datasets, including SLOPE, LEAD, Rural Atlas, EJScreen, and Census data. 

   more » « less