ArticleProceedings of the ACM on management of data2023
A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy.
Article in Proceedings of the ACM on management of data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Toward Foundation Models for Mobility Enriched Geospatially Embedded Objects.Proceedings of the ... ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems : ACM GIS. ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems · 2025Article
- BiasBuster: a Neural Approach for Accurate Estimation of Population Statistics using Biased Location Data.IEEE International Conference on Mobile Data Management : MDM : [proceedings]. IEEE International Conference on Mobile Data Management · 2024Article
- NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks.Proceedings of the ACM on management of data · 2023Article
- A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy.Proceedings of the ACM on management of data · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Several companies (e.g., Meta, Google) have initiated "data-for-good" projects where aggregate location data are first sanitized and released publicly, which is useful to many applications in transportation, public health (e.g., COVID-19 spread) and urban planning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.