Search results for "anonymization"

showing 5 items of 5 documents

Discovery privacy threats via device de-anonymization in LoRaWAN

2021

LoRaWAN (Long Range WAN) is one of the well-known emerging technologies for the Internet of Things (IoT). Many IoT applications involve simple devices that transmit their data toward network gateways or access points that, in their turn, redirect data to application servers. While several security issues have been addressed in the LoRaWAN specification v1.1, there are still some aspects that may undermine privacy and security of the interconnected IoT devices. In this paper, we tackle a privacy aspect related to LoRaWAN device identity. The proposed approach, by monitoring the network traffic in LoRaWAN, is able to derive, in a probabilistic way, the unique identifier of the IoT device from…

Information privacyIoTDe-anonymizationde-anonymizationsComputer scienceEmerging technologiesComputer Networks and CommunicationsInternet of ThingsDevice identificationcomputer.software_genreComputer securityprivacyLoRaSecurity and privacyUnique identifierDe-anonymizationLoRaWAN; Security; privacy; de-anonymizationsLorawanApplication serverNetwork packetProbabilistic logicIdentification (information)internet of things; lora; lorawan; security; privacy; network optimizationSecuritycomputerNetwork optimizationComputer Communications
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Towards better privacy preservation by detecting personal events in photos shared within online social networks

2015

Today, social networking has considerably changed why people are taking pictures all the time everywhere they go. More than 500 million photos are uploaded and shared every day, along with more than 200 hours of videos every minute. More particularly, with the ubiquity of smartphones, social network users are now taking photos of events in their lives, travels, experiences, etc. and instantly uploading them online. Such public data sharing puts at risk the users’ privacy and expose them to a surveillance that is growing at a very rapid rate. Furthermore, new techniques are used today to extract publicly shared data and combine it with other data in ways never before thought possible. Howeve…

CybersecurityDétection d’évènements[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH]Cyber SecurityEvent relationsData analysisRéseaux sociauxAnonymization[INFO] Computer Science [cs]Regroupement d’imagesRelations entre les évènementsCyber sécuritéPrivacy protection[INFO]Computer Science [cs]Protection de la vie privéeOnline social networksDétection d'évènementsMetadataAnonymisationIdentité numériqueAnalyse de donnéesOnline identityImage clustering[INFO.INFO-OH] Computer Science [cs]/Other [cs.OH]MétadonnéesSocial Networks[ INFO.INFO-OH ] Computer Science [cs]/Other [cs.OH]Event detection
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Ethics appraisal procedure in 79,670 Marie Skłodowska-Curie proposals from the entire European HORIZON 2020 research and innovation program (2014–202…

2021

Introduction Horizon 2020 was the most significant EU Research and Innovation programme ever implemented and included the Marie Skłodowska-Curie Actions (MSCA). Proposals submitted to the MSCA actions awere subject to the Ethics Appraisal Procedure. In this work we explored the ethics appraisal procedure in MSCA H2020. Methods Using a retrospective analysis of pooled anonymized data, we explored the ethics appraisal procedure on proposals submitted to Marie Skłodowska-Curie Actions (MSCA) during the entire Horizon 2020 program period (N = 79,670). Results Our results showed that one of the most frequently identified ethics categories was Data protection. We also detected slight differences…

Science and Technology WorkforceScience PolicyScienceLegislationPublication EthicsSocial SciencesCareers in ResearchResearch EthicsGeographical locationsEthics ResearchInformed consentData AnonymizationMedicine and Health SciencesCurieRetrospective analysisData Protection Act 1998Public and Occupational HealthEuropean UnionCell Cycle and Cell DivisionResearch IntegrityRetrospective StudiesMedical educationResearch ethicsMultidisciplinaryHorizon (archaeology)QMSCA H2020 Ethics appraisal procedure research integrityRBiology and Life SciencesSubject (documents)Cell BiologyEuropeHealth CareWork (electrical)Cell ProcessesMedicineLaw and Legal SciencesPeople and placesPsychologyEnvironmental HealthResearch ArticlePLOS ONE
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Using privacy-transformed speech in the automatic speech recognition acoustic model training

2020

Automatic Speech Recognition (ASR) requires huge amounts of real user speech data to reach state-of-the-art performance. However, speech data conveys sensitive speaker attributes like identity that can be inferred and exploited for malicious purposes. Therefore, there is an interest in the collection of anonymized speech data that is processed by some voice conversion method. In this paper, we evaluate one of the voice conversion methods on Latvian speech data and also investigate if privacy-transformed data can be used to improve ASR acoustic models. Results show the effectiveness of voice conversion against state-of-the-art speaker verification models on Latvian speech and the effectivene…

Speaker verificationevaluationvoice conversionComputer scienceSpeech recognitionautomatic speech recognitionLatvianAcoustic model[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]privacylanguage.human_language[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]anonymization[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG][INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL]Identity (object-oriented programming)languageConversion methodautomatic speaker verification
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Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing

2023

Current advances in machine (deep) learning and the exponential growth of data collected by and shared between smart manufacturing processes give a unique opportunity to get extra value from that data. The use of public machine learning services actualizes the issue of data privacy. Ordinary encryption protects the data but could make it useless for the machine learning objectives. Therefore, “privacy of data vs. value from data” is the major dilemma within the privacy preserving machine learning activity. Special encryption techniques or synthetic data generation are being in focus to address the issue. In this paper, we discuss a complex hybrid protection algorithm, which assumes sequenti…

data privacyIndustry 4.0anonymizationimage processingtietosuojakoneoppiminensalausautoencoderssyntetic data generationGeneral Earth and Planetary SciencesvalmistustekniikkakonenäköteollisuusanonymiteettiGeneral Environmental ScienceProcedia Computer Science
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