- author = "Silvia Perelli and Eric Medvet and Vincenzo Gulisano",
- title = "{SHIELD}: Evolutionary Synthesis of Privacy-Preserving Pipelines for Live Stream Data Sharing",
- booktitle = "Proceedings of the 20th ACM International Conference on Distributed and Event-Based Systems",
- year = "2026",
- editor = "Hugo Miranda and Nuno Cruz",
- pages = "82--94",
- address = "Lisbon, Portugal",
- month = jun # " 23-26",
- publisher = "Association for Computing Machinery",
- keywords = "genetic algorithms, genetic programming, CFG-GP, Stream Processing; Evolutionary Computing, CFG, context-free grammar, NSGA-II, BNF, Temporal aggregation, Objective ablation, FMA, GpeLife, AirQuality, GPS",
- series = "DEBS '26",
- isbn13 = "9798400726934",
-
URL = "
https://medvet.inginf.units.it/publications/2026-c-pmg-shield/",
-
URL = "
https://research.chalmers.se/publication/553353/file/553353_Fulltext.pdf",
-
URL = "https://arts.units.it/handle/11368/3140059",
-
DOI = "
10.1145/3809481.3812614",
-
code_url = "https://zenodo.org/records/20348184",
- size = "13 pages",
-
abstract = "Modern data-driven organizations rely on pipelines
that transform data from infrastructure, applications,
and users, where correctness and performance are
critical for reliability and business value. These
pipelines are often developed and optimized by teams
separate from the data owners defining semantics, so
meaningful testing and benchmarking require sharing
representative data, even though real data is often
sensitive and restricted by legal and commercial
constraints.
To enable privacy-preserving data sharing while preserving utility, our framework, named SHIELD, automatically constructs stream processing (SP) pipelines to transform live sensitive data into shareable data while preserving the characteristics required for downstream processing and optimization. Internally, SHIELD leverages evolutionary computation to synthesize executable SP queries under predefined privacy and utility requirements. Using real-world use cases, we show SHIELD can synthesize privacy-preserving pipelines that retain analytical value and scale to realistic workloads.",
- notes = "ACM Artifact available v1.1",
