Soufargi, SelimFortino, GiancarloCuzzocrea, Alfredo2026-04-102024-05-15http://hdl.handle.net/10955/5759Università della Calabria. Dipartimento di ingegneria Informatica, Modellistica, Elettronica e Sistemistica. Dottorato di ricerca in: Information and Communication Technologies. Ciclo XXXVIIt iswellestablishedthathealthinformationprivacyisofcrucialvalueand importancetothegoodexecutionofanalyticalprocesses.Theroleofanalyt- ics, ontheotherhand,isalsoknowntobecriticalforprecisionmedicineas wellasforaccuratehealthcarerecommendations.Whiledataanalyticstryto uncoverpatternsintheunderlyinghealthdatatosupportdecisionmaking, privacy,priorly,ensuresthatthesedataarenotexposingsensitiveinforma- tion abouttheindividualsonwhichtheanalyticalprocessisbeingapplied. Unfortunately,existingresearchworksusuallyputemphasisonsolvingei- ther dataprivacyordataanalyticsissuesinsuchawaythatleadstopoor analytical outcomesintermsofaccuracyfordecisionmakingsupport.This challengemotivatesourneedforajointparadigmbetweendataprivacyand data analyticstoenhancethecapabilitiesofcurrentanalysisofhealthcare data toeventuallyenableprecisionmedicine.Indeed,ajointparadigmwould involvethecreationofalgorithmsthatconsiderafullyintegratedprocessthat enables dataanalyticswhilepreventingthedisclosureofidentityinformation. In thisthesis,weproposeseveralalgorithms,frameworksandtechniquesthat speci callyaddressthepreviousmattersandchallengesindatalakecontexts. Indeed, weaimatdevelopingprivacy-preservingdataanalyticstechniquesin big datalakeenvironmentsbasedondi erentkindsofdatatypesandsettings. In fact,dependingonthegoaloftheprivacypreservinganalyticaltasks,we proposetailoredframeworksthat,weargue,e ectivelyande cientlysupport the creationofhealthrelatedrecommendationsandthus,intheQUALITOP context,supportqualityoflifeaftertreatmentsforcancerpatients.enBig data. Big data lakes. Big data privacy. Big data analyticsThesis