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Syst."],"published-print":{"date-parts":[[2026,7,31]]},"abstract":"<jats:p>\n                    Modern publish-subscribe systems are increasingly deployed in applications such as smart grids, industrial IoT, and smart homes, where brokers perform not only message forwarding but also in-broker computation. This shift from receive-forward (RF) to receive-compute-forward (RCF) models introduces new privacy and trust concerns, as centralized brokers gain access to sensitive data. In this article, we present a distributed and privacy-preserving RCF (\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\textsf{DRCF}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ) framework that replaces the trust assumption on a single centralized broker with multiple semi-honest brokers. Our system integrates distributed authenticated encryption, threshold signatures, and 2-party secure computation realized using homomorphic encryption and garbled circuits to protect both communication and computation. We formally prove the security of\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\textsf{DRCF}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    in the semi-honest adversarial model. We implement\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\textsf{DRCF}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    on top of the\n                    <jats:sans-serif>Mosquitto<\/jats:sans-serif>\n                    broker and evaluate it on real-world IoT workloads, including anomaly detection, load forecasting, and electricity price prediction. Experimental results demonstrate that\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\textsf{DRCF}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    achieves scalability and efficiency while preserving privacy, confidentiality, and integrity in modern publish-subscribe systems.\n                  <\/jats:p>","DOI":"10.1145\/3805045","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:22:31Z","timestamp":1774621351000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["DRCF: A Privacy-Enhanced Distributed Publish-Subscribe System for Secure Data Computation in Untrusted Environments"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3422-2216","authenticated-orcid":false,"given":"Shabnam Saderi","family":"Oskouei","sequence":"first","affiliation":[{"name":"Canadian Institute for Cybersecurity, Faculty of Computer Science, University of New Brunswick, Fredericton, New Brunswick, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8228-5016","authenticated-orcid":false,"given":"Kalikinkar","family":"Mandal","sequence":"additional","affiliation":[{"name":"Canadian Institute for Cybersecurity, Faculty of Computer Science, University of New Brunswick, Fredericton, New Brunswick, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9189-6268","authenticated-orcid":false,"given":"Ali A.","family":"Ghorbani","sequence":"additional","affiliation":[{"name":"Canadian Institute for Cybersecurity, Faculty of Computer Science, University of New Brunswick, Fredericton, New Brunswick, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40595-018-0119-7"},{"key":"e_1_3_2_3_2","unstructured":"Apple Inc. 2024. 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