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Source:
Wang , Y , Kanwal , N , Engan , K , Rong , C & Zhao , Z 2023 , Towards a Privacy-Preserving Distributed Cloud Service for Preprocessing
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Chabal , D , Sapra , D & Mann , Z A 2023 ' On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence ' v2 edn .
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Spini , G , Mancini , E , Attema , T , Abspoel , M , de Gier , J , Fehr , S , Veugen , T , van Heesch , M , Worm , D , De Luca , A , Cramer , R & Sloot , P M A 2022 , ' New Approach to
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Hu , Z , Havrylov , S , Titov , I & Cohen , S B 2020 , Obfuscation for privacy-preserving syntactic parsing . in G Bouma , Y Matsumoto , S
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Boeschoten , L , Ausloos , J , Möller , J E , Araujo , T & Oberski , D L 2020 ' A framework for privacy preserving digital trace data
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Dehghani , M , Azarbonyad , H , Kamps , J & de Rijke , M 2017 , Share your Model instead of your Data: Privacy Preserving Mimic Learning
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Foulds , J , Geumlek , J , Welling , M & Chaudhuri , K R 2016 , On the Theory and Practice of Privacy Preserving Data Analysis . in A
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Lotfian Delouee , M , Degeler , V , Amthor , P & Koldehofe , B 2024 , APP-CEP: Adaptive Pattern-level Privacy Protection in Complex Event Processing Systems . in G
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Amiri , S , Belloum , A , Nalisnick , E , Klous , S & Gommans , L 2022 , On the impact of non-IID data on the performance and fairness of differentially private federated learning . in Proceedings,
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Park , M , Foulds , J , Chaudhuri , K & Welling , M 2017 , ' DP-EM: Differentially Private Expectation Maximization ' , Proceedings of Machine Learning Research , vol. 54 , pp. 896-904 . <
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