The Web Conference, the leading scientific forum on the web and related technologies, ran from April 20th to April 24th. Initially planned to be held in Taiwan, the conference moved to an online setting due to the COVID-19 pandemic. In this unusual setting, DDIS presented a study on differentially-private stream processing for the semantic web. Daniele Dell’Aglio and Abraham Bernstein tackled the problem of continuous publication of statistics extracted by data streams containing sensitive data by proposing a query language, SihlQL, and a novel algorithm, the bin removal mechanism. SihlQL is built on a fragment of SPARQL compatible with differential privacy, while the bin removal mechanism protects users behaving differently from the majority of the populations.

More information about this study is available in the article here and in the presentation that Daniele gave at the conference can be found by clicking the image below.

Finally, users interested in trying out SihlQL can find SihlMill, a SihlQL engine here.

This study is partially supported by the Swiss National Science Foundation under contract number #407550_167177 NFP 75‘s project Privacy-preserving, stream analytics for non-computer scientists.