Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics

Privacy poses a significant obstacle to the progress of learning analytics (LA), presenting challenges like inadequate anonymization and data misuse that current solutions struggle to address. Synthetic data emerges as a potential remedy, offering robust privacy protection. However, prior LA research on synthetic data lacks thorough evaluation, essential for assessing the delicate balance between privacy and data utility. Synthetic data must not only enhance privacy but also remain practical for data analytics. Moreover, diverse LA scenarios come with varying privacy and utility needs, making the selection of an appropriate synthetic data approach a pressing challenge. To address these gaps, we propose a comprehensive evaluation of synthetic data, which encompasses three dimensions of synthetic data quality, namely resemblance, utility, and privacy. We apply this evaluation to three distinct LA datasets, using three different synthetic data generation methods. Our results show that synthetic data can maintain similar utility (i.e., predictive performance) as real data, while preserving privacy. Furthermore, considering different privacy and data utility requirements in different LA scenarios, we make customized recommendations for synthetic data generation. This paper not only presents a comprehensive evaluation of synthetic data but also illustrates its potential in mitigating privacy concerns within the field of LA, thus contributing to a wider application of synthetic data in LA and promoting a better practice for open science

Azithromycin and cefixime combination versus azithromycin alone for the out-patient treatment of clinically suspected or confirmed uncomplicated typhoid fever in South Asia: a randomised controlled trial protocol.

Background: Typhoid and paratyphoid fever (enteric fever) is a common cause of non-specific febrile infection in adults and children presenting to health care facilities in low resource settings such as the South Asia.  A 7-day course of a single oral antimicrobial such as ciprofloxacin, cefixime, or azithromycin is commonly used for its treatment. Increasing antimicrobial resistance threatens the effectiveness of these treatment choices. We hypothesize that combined treatment with azithromycin (active mainly intracellularly) and cefixime (active mainly extracellularly) will be a better option for the treatment of clinically suspected and culture-confirmed typhoid fever in South Asia. Methods: This is a phase IV, international multi-center, multi-country, comparative participant-and observer-blind, 1:1 randomised clinical trial. Patients with suspected uncomplicated typhoid fever will be randomized to one of the two interventions: Arm A: azithromycin 20mg/kg/day oral dose once daily (maximum 1gm/day) and cefixime 20mg/kg/day oral dose in two divided doses (maximum 400mg bd) for 7 days, Arm B: azithromycin 20mg/kg/day oral dose once daily (max 1gm/day) for 7 days AND cefixime-matched placebo for 7 days. We will recruit 1500 patients across sites in Bangladesh, India, Nepal, and Pakistan. We will assess whether treatment outcomes are better with the combination after one week of treatment and at one- and three-months follow-up. Discussion: Combined treatment may limit the emergence of resistance if one of the components is active against resistant sub-populations not covered by the other antimicrobial activity. If the combined treatment is better than the single antimicrobial treatment, this will be an important result for patients across South Asia and other typhoid endemic areas. Clinicaltrials.gov registration: NCT04349826 (16/04/2020)