Search for: All records

Among the most challenging traffic-analysis attacks to confound are those leveraging the sizes of objects downloaded over the network, as the size of an object is often a powerful indicator of its identity. In this dissertation, we consider this challenge in both (i) the simplified setting where successive object retrievals are assumed to be independent and (ii) the setting where sequential object retrievals are dependent on one another. Furthermore, within the dependent retrievals setting, we address the scenario where enumerating all possible sequences is impractical. For each setting, we present algorithms by which a benevolent object store computes a memoryless padding scheme to pad objects before sending them, in a way that bounds the information gain that the padded sizes provide to the network observer about the objects being retrieved. Furthermore, all of our algorithms ensure that no object is padded to more than c× its original size, for a tunable factor c > 1. We compare each algorithm to recent contenders in the research literature and evaluate their performance on practical datasets. 

The Ocean Observatories Initiative (OOI) deployed both the In-Situ Ultraviolet Spectrophotometer (ISUS) and Submersible Underwater Nitrate Sensor (SUNA) for continuous, in-situ measurement of nitrate. At the Pioneer-New England Shelf Array (Pioneer-NES), ISUS/SUNA sensors were deployed at 7-meters depth at the Inshore (ISSM), Central (CNSM), and Offshore (OSSM) Surface Mooring locations. The SUNA sensor replaced the ISUS sensors spring 2018. The SUNA was a major improvement in technology, with significant improvements in accuracy and precision. However, it still suffers from calibration drift due to lamp fatigue and biofouling as well as spectral interference due to bromide and fluorometric CDOM. Drift is corrected by application of post-cruise calibrations to recalculate the temperature-and-salinity corrected nitrate concentration following Sakamoto (2009a) and estimating a linear drift between pre-and-post cruise deployments. Validation is performed by comparison with discrete water samples collected during deployment/recovery of the sensors. These datasets include the nitrate data from the Pioneer-NES ISSM (CP03ISSM-RID26-07-NUTNRB000.nc), CNSM (CP01CNSM-RID26-07-NUTNRB000.nc), and OSSM (CP04OSSM-RID26-07-NUTNRB000.nc) SUNA instruments spanning Spring 2018 through Fall 2022. Each dataset contains the measured nitrate, the temperature-salinity corrected nitrate, the drift-corrected nitrate, and the nitrate following validation with bottle samples. 

The field of oceanography is transitioning from data-poor to data-rich, thanks in part to increased deployment ofin-situplatforms and sensors, such as those that instrument the US-funded Ocean Observatories Initiative (OOI). However, generating science-ready data products from these sensors, particularly those making biogeochemical measurements, often requires extensive end-user calibration and validation procedures, which can present a significant barrier. Openly available community-developed and -vetted Best Practices contribute to overcoming such barriers, but collaboratively developing user-friendly Best Practices can be challenging. Here we describe the process undertaken by the NSF-funded OOI Biogeochemical Sensor Data Working Group to develop Best Practices for creating science-ready biogeochemical data products from OOI data, culminating in the publication of the GOOS-endorsed OOI Biogeochemical Sensor Data Best Practices and User Guide. For Best Practices related to ocean observatories, engaging observatory staff is crucial, but having a “user-defined” process ensures the final product addresses user needs. Our process prioritized bringing together a diverse team and creating an inclusive environment where all participants could effectively contribute. Incorporating the perspectives of a wide range of experts and prospective end users through an iterative review process that included “Beta Testers’’ enabled us to produce a final product that combines technical information with a user-friendly structure that illustrates data analysis pipelines via flowcharts and worked examples accompanied by pseudo-code. Our process and its impact on improving the accessibility and utility of the end product provides a roadmap for other groups undertaking similar community-driven activities to develop and disseminate new Ocean Best Practices.