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In their seminal work, Tramer and Boneh presented the Slalom protocol for privacy-preserving inference by splitting the computation into a data-independent preprocessing phase and a very efficient online phase. In this work, we present a new method to significantly speed up the preprocessing phase  by introducing the  Carnival  protocol.  Carnival  leverages the pseudo-randomness of the Subset sum problem to also enable efficient outsourcing during the preprocessing phase. In addition to a security proof we also include an empirical study analyzing the landscape of the uniformity of the output of the Subset sum function for smaller parameters. Our findings  show that  Carnival is a great candidate for real-world implementations. <\/jats:p>","DOI":"10.62056\/akp-49qgxq","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T15:13:33Z","timestamp":1728314013000},"update-policy":"https:\/\/doi.org\/10.62056\/adfjwm02dj","source":"Crossref","is-referenced-by-count":0,"title":["Slalom at the Carnival: Privacy-preserving Inference with Masks from Public Knowledge"],"prefix":"10.62056","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2020-8012","authenticated-orcid":false,"given":"Ida","family":"Bruhns","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00t3r8h32","id-type":"ROR","asserted-by":"publisher"}],"name":"Universit\u00e4t zu L\u00fcbeck","place":["L\u00fcbeck, Germany"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4177-8081","authenticated-orcid":false,"given":"Sebastian","family":"Berndt","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/032xqbj11","id-type":"ROR","asserted-by":"publisher"}],"name":"Technical University of Applied Sciences L\u00fcbeck","place":["L\u00fcbeck, Germany"]}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9402-823X","authenticated-orcid":false,"given":"Jonas","family":"Sander","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00t3r8h32","id-type":"ROR","asserted-by":"publisher"}],"name":"Universit\u00e4t zu L\u00fcbeck","place":["L\u00fcbeck, Germany"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1116-6973","authenticated-orcid":false,"given":"Thomas","family":"Eisenbarth","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00t3r8h32","id-type":"ROR","asserted-by":"publisher"}],"name":"Universit\u00e4t zu L\u00fcbeck","place":["L\u00fcbeck, Germany"]}]}],"member":"48349","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"ref1:ng2023sokcryptographic","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1109\/SP46215.2023.10179483","article-title":"SoK: Cryptographic Neural-Network Computation","author":"Lucien K. 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