Дисертації з теми "Data and information privacy"
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Aron, Yotam. "Information privacy for linked data." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/85215.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 77-79).
As data mining over massive amounts of linked data becomes more and more prevalent in research applications, information privacy becomes a more important issue. This is especially true in the biological and medical fields, where information sensitivity is high. Previous experience has shown that simple anonymization techniques, such as removing an individual's name from a data set, are inadequate to fully protect the data's participants. While strong privacy guarantees have been studied for relational databases, these are virtually non-existent for graph-structured linked data. This line of research is important, however, since the aggregation of data across different web sources may lead to privacy leaks. The ontological structure of linked data especially aids these attacks on privacy. The purpose of this thesis is two-fold. The first is to investigate differential privacy, a strong privacy guarantee, and how to construct differentially-private mechanisms for linked data. The second involves the design and implementation of the SPARQL Privacy Insurance Module (SPIM). Using a combination of well-studied techniques, such as authentication and access control, and the mechanisms developed to maintain differential privacy over linked data, it attempts to limit privacy hazards for SPARQL queries. By using these privacy-preservation techniques, data owners may be more willing to share their data sets with other researchers without the fear that it will be misused. Consequently, we can expect greater sharing of information, which will foster collaboration and improve the types of data that researchers can have access to.
by Yotam Aron.
M. Eng.
El-Sheikh, Mahmoud Mohamed Omar. "Developing a Libyan information privacy framework." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/65866/1/Mahmoud%20Mohamed%20Omar_El-Sheikh_Thesis.pdf.
Повний текст джерелаAn, Nan. "Protect Data Privacy in E-Healthcare in Sweden." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-1619.
Повний текст джерелаSweden healthcare adopted much ICT (information and communication technology). It is a highly information intensive place. This thesis gives a brief description of the background of healthcare in Sweden and ICT adoption in healthcare, introduces an Information system security model, describes the technology and law about data privacy and carries out a case through questionnaire and interview.
Sang, Lin. "Social Big Data and Privacy Awareness." Thesis, Uppsala universitet, Institutionen för informatik och media, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242444.
Повний текст джерелаZheng, Yao. "Privacy Preservation for Cloud-Based Data Sharing and Data Analytics." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/73796.
Повний текст джерелаPh. D.
Sivakumar, Anusha. "Enhancing Privacy Of Data Through Anonymization." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177349.
Повний текст джерелаEn kraftig ökning av tillgång på personligt relaterat data, har lett till oändliga möjligheter för dataforskare att utnyttja dessa data för forskning. En konsekvens är att det blir svårt att bevara personers integritet på grund av den enorma mängd uppgifter som är tillgängliga. För att skydda den personliga integriteten finns möjligheten att med traditionella metoder använda pseudonymer och alias, innan personen publicerar personligt data. Att enbart använda dessa traditionella metoder är inte tillräckligt för att skydda privatlivet, det finns alltid möjligheter att koppla data till verkliga individer. En potentiell lösning på detta problem är att använda anonymiseringstekniker, för att förändra data om individen på att anpassat sätt och på det viset försvåra att data sammankopplas med en individ. Vid undersökningar som innehåller personuppgifter blir anonymisering allt viktigare. Om vi försöker att ändra uppgifter för att bevara integriteten av forskningsdeltagare innan data publiceras, blir den resulterande uppgifter nästan oanvändbar för många undersökningar. För att bevara integriteten av individer representerade i underlaget och att minimera dataförlust orsakad av privatlivet bevarande är mycket viktigt. I denna avhandling har vi studerat de olika fall där attackerna kan ske, olika former av attacker och befintliga lösningar för att förhindra attackerna. Efter att noggrant granskat litteraturen och problemet, föreslår vi en teoretisk lösning för att bevara integriteten av forskningsdeltagarna så mycket som möjligt och att uppgifterna ska vara till nytta för forskning. Som stöd för vår lösning, gällande digitala fotspår som lagrar Facebook uppgifter med samtycke av användarna och släpper den lagrade informationen via olika användargränssnitt.
Smith, Tanshanika Turner. "Examining Data Privacy Breaches in Healthcare." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/2623.
Повний текст джерелаKatsikouli, Panagiota. "Distributed and privacy preserving algorithms for mobility information processing." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/31110.
Повний текст джерелаGonçalves, João Miguel Ribeiro. "Context-awareness privacy in data communications." Doctoral thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/15760.
Повний текст джерелаInternet users consume online targeted advertising based on information collected about them and voluntarily share personal information in social networks. Sensor information and data from smart-phones is collected and used by applications, sometimes in unclear ways. As it happens today with smartphones, in the near future sensors will be shipped in all types of connected devices, enabling ubiquitous information gathering from the physical environment, enabling the vision of Ambient Intelligence. The value of gathered data, if not obvious, can be harnessed through data mining techniques and put to use by enabling personalized and tailored services as well as business intelligence practices, fueling the digital economy. However, the ever-expanding information gathering and use undermines the privacy conceptions of the past. Natural social practices of managing privacy in daily relations are overridden by socially-awkward communication tools, service providers struggle with security issues resulting in harmful data leaks, governments use mass surveillance techniques, the incentives of the digital economy threaten consumer privacy, and the advancement of consumergrade data-gathering technology enables new inter-personal abuses. A wide range of fields attempts to address technology-related privacy problems, however they vary immensely in terms of assumptions, scope and approach. Privacy of future use cases is typically handled vertically, instead of building upon previous work that can be re-contextualized, while current privacy problems are typically addressed per type in a more focused way. Because significant effort was required to make sense of the relations and structure of privacy-related work, this thesis attempts to transmit a structured view of it. It is multi-disciplinary - from cryptography to economics, including distributed systems and information theory - and addresses privacy issues of different natures. As existing work is framed and discussed, the contributions to the state-of-theart done in the scope of this thesis are presented. The contributions add to five distinct areas: 1) identity in distributed systems; 2) future context-aware services; 3) event-based context management; 4) low-latency information flow control; 5) high-dimensional dataset anonymity. Finally, having laid out such landscape of the privacy-preserving work, the current and future privacy challenges are discussed, considering not only technical but also socio-economic perspectives.
Quem usa a Internet vê publicidade direccionada com base nos seus hábitos de navegação, e provavelmente partilha voluntariamente informação pessoal em redes sociais. A informação disponível nos novos telemóveis é amplamente acedida e utilizada por aplicações móveis, por vezes sem razões claras para isso. Tal como acontece hoje com os telemóveis, no futuro muitos tipos de dispositivos elecónicos incluirão sensores que permitirão captar dados do ambiente, possibilitando o surgimento de ambientes inteligentes. O valor dos dados captados, se não for óbvio, pode ser derivado através de técnicas de análise de dados e usado para fornecer serviços personalizados e definir estratégias de negócio, fomentando a economia digital. No entanto estas práticas de recolha de informação criam novas questões de privacidade. As práticas naturais de relações inter-pessoais são dificultadas por novos meios de comunicação que não as contemplam, os problemas de segurança de informação sucedem-se, os estados vigiam os seus cidadãos, a economia digital leva á monitorização dos consumidores, e as capacidades de captação e gravação dos novos dispositivos eletrónicos podem ser usadas abusivamente pelos próprios utilizadores contra outras pessoas. Um grande número de áreas científicas focam problemas de privacidade relacionados com tecnologia, no entanto fazem-no de maneiras diferentes e assumindo pontos de partida distintos. A privacidade de novos cenários é tipicamente tratada verticalmente, em vez de re-contextualizar trabalho existente, enquanto os problemas actuais são tratados de uma forma mais focada. Devido a este fraccionamento no trabalho existente, um exercício muito relevante foi a sua estruturação no âmbito desta tese. O trabalho identificado é multi-disciplinar - da criptografia à economia, incluindo sistemas distribuídos e teoria da informação - e trata de problemas de privacidade de naturezas diferentes. À medida que o trabalho existente é apresentado, as contribuições feitas por esta tese são discutidas. Estas enquadram-se em cinco áreas distintas: 1) identidade em sistemas distribuídos; 2) serviços contextualizados; 3) gestão orientada a eventos de informação de contexto; 4) controlo de fluxo de informação com latência baixa; 5) bases de dados de recomendação anónimas. Tendo descrito o trabalho existente em privacidade, os desafios actuais e futuros da privacidade são discutidos considerando também perspectivas socio-económicas.
Dayarathna, Rasika. "Discovering Constructs and Dimensions for Information Privacy Metrics." Doctoral thesis, Stockholms universitet, Institutionen för data- och systemvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-89336.
Повний текст джерелаAt the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 6: Accepted.
Rohunen, A. (Anna). "Advancing information privacy concerns evaluation in personal data intensive services." Doctoral thesis, Oulun yliopisto, 2019. http://urn.fi/urn:isbn:9789526224688.
Повний текст джерелаTiivistelmä Kun henkilötietoja kerätään ja hyödynnetään dataintensiivisten palveluiden tuottamiseen, palveluiden käyttäjien tietosuoja saattaa heikentyä. Käyttäjien tietosuojahuolet voivat hidastaa uusien palveluiden ja teknologioiden käyttöönottoa sekä vaikuttaa kerättävän tiedon laatuun ja kattavuuteen. Tämä hankaloittaa palveluiden täysimittaista hyödyntämistä. Tietosuojahuolten arviointi mahdollistaa niiden huomioimisen henkilötietoperusteisten palveluiden suunnittelussa ja kehittämisessä. Tässä tutkimuksessa selvitettiin, kuinka tietosuojahuolten arviointia tulisi kehittää muuttuvissa tiedonkeruuympäristöissä. Kaksivaiheisessa tutkimuksessa toteutettiin aluksi empiirinen monimenetelmällinen tutkimus ja tämän jälkeen systemaattinen kirjallisuustutkimus. Ensimmäisessä vaiheessa tehtiin kaksi empiiristä tutkimusta monimenetelmällisen tutkimuksen tutkivan peräkkäisen asetelman mukaisesti. Näissä tutkimuksissa selvitettiin ensin laadullisin menetelmin tietosuojakäyttäytymistä ja tietosuojahuolia liikkumisen dataa kerättäessä. Laadullisten tulosten pohjalta kehitettiin kvantitatiiviset instrumentit tulosten yleistettävyyden tutkimiseksi. Tutkimuksen toisessa vaiheessa toteutettiin kaksi katsaustyyppistä tutkimusta, jotta saataisiin kattava käsitys tietosuojakäyttäytymisestä sekä mahdollisuuksista kehittää tietosuojahuolten arviointia uusissa tiedonkeruuympäristöissä. Nämä tutkimukset olivat systemaattinen kirjallisuuskatsaus tietosuojakäyttäytymisen malleista sekä katsaus EU:n tietosuojalainsäädännön muutoksista. Tutkimuksen tulokset osoittavat, että kehittyvissä tiedonkeruuympäristöissä tietosuojakäyttäytyminen ja tietosuojahuolet poikkeavat aikaisemmista ympäristöistä. Näissä ympäristöissä esiintyy niille ominaisia tietosuojahuolia ja huolten monitahoisuus korostuu. Koska tietosuojahuolet ovat kytköksissä muihin tietosuojakäyttäytymistä ennustaviin muuttujiin, arviointeihin voi olla aiheellista sisällyttää myös näitä muuttujia. Olemassa olevia tietosuojahuolten arviointi-instrumentteja on perusteltua käyttää arvioinnin lähtökohtana myös kehittyvissä tiedonkeruuympäristöissä, mutta niitä on mukautettava uusiin ympäristöihin soveltuviksi. Arvioinnin kehittäminen voi olla haasteellista, sillä aikaisempi tietosuojatutkimus on epäyhtenäistä. Jotta sitä voidaan soveltaa asianmukaisesti arviointien kehittämisessä, tutkimusta on vietävä kokonaisvaltaisempaan suuntaan
Olsson, Mattias. "Klassificeringsalgoritmer vs differential privacy : Effekt på klassificeringsalgoritmer vid användande av numerisk differential privacy." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-15680.
Повний текст джерелаMao, Congcong. "Privacy Issues in IoT : Privacy concerns in smart home." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-90587.
Повний текст джерелаMivule, Kato. "An investigation of data privacy and utility using machine learning as a gauge." Thesis, Bowie State University, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3619387.
Повний текст джерелаThe purpose of this investigation is to study and pursue a user-defined approach in preserving data privacy while maintaining an acceptable level of data utility using machine learning classification techniques as a gauge in the generation of synthetic data sets. This dissertation will deal with data privacy, data utility, machine learning classification, and the generation of synthetic data sets. Hence, data privacy and utility preservation using machine learning classification as a gauge is the central focus of this study. Many organizations that transact in large amounts of data have to comply with state, federal, and international laws to guarantee that the privacy of individuals and other sensitive data is not compromised. Yet at some point during the data privacy process, data loses its utility - a measure of how useful a privatized dataset is to the user of that dataset. Data privacy researchers have documented that attaining an optimal balance between data privacy and utility is an NP-hard challenge, thus an intractable problem. Therefore we propose the classification error gauge (x-CEG) approach, a data utility quantification concept that employs machine learning classification techniques to gauge data utility based on the classification error. In the initial phase of this proposed approach, a data privacy algorithm such as differential privacy, Gaussian noise addition, generalization, and or k-anonymity is applied on a dataset for confidentiality, generating a privatized synthetic data set. The privatized synthetic data set is then passed through a machine learning classifier, after which the classification error is measured. If the classification error is lower or equal to a set threshold, then better utility might be achieved, otherwise, adjustment to the data privacy parameters is made and then the refined synthetic data set is sent to the machine learning classifier; the process repeats until the error threshold is reached. Additionally, this study presents the Comparative x-CEG concept, in which a privatized synthetic data set is passed through a series of classifiers, each of which returns a classification error, and the classifier with the lowest classification error is chosen after parameter adjustments, an indication of better data utility. Preliminary results from this investigation show that fine-tuning parameters in data privacy procedures, for example in the case of differential privacy, and increasing weak learners in the ensemble classifier for instance, might lead to lower classification error, thus better utility. Furthermore, this study explores the application of this approach by employing signal processing techniques in the generation of privatized synthetic data sets and improving data utility. This dissertation presents theoretical and empirical work examining various data privacy and utility methodologies using machine learning classification as a gauge. Similarly this study presents a resourceful approach in the generation of privatized synthetic data sets, and an innovative conceptual framework for the data privacy engineering process.
Spiekermann-Hoff, Sarah, and Alexander Novotny. "A vision for global privacy bridges: Technical and legal measures for international data markets." Elsevier, 2015. http://dx.doi.org/10.1016/j.clsr.2015.01.009.
Повний текст джерелаRuiz, Nicolas. "Toward a universal privacy and information-preserving framework for individual data exchange." Doctoral thesis, Universitat Rovira i Virgili, 2019. http://hdl.handle.net/10803/666489.
Повний текст джерелаLiu, Lian. "PRIVACY PRESERVING DATA MINING FOR NUMERICAL MATRICES, SOCIAL NETWORKS, AND BIG DATA." UKnowledge, 2015. http://uknowledge.uky.edu/cs_etds/31.
Повний текст джерелаAwwal, Mohammad Abdul. "An Empirical Investigation of the Relationship between Computer Self-Efficacy and Information Privacy Concerns." NSUWorks, 2011. http://nsuworks.nova.edu/gscis_etd/82.
Повний текст джерелаBalan, Khalil. "User perspective of privacy and surveillance on social networks." Thesis, Linnéuniversitetet, Institutionen för informatik (IK), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-63876.
Повний текст джерелаBasciftci, Yuksel O. Basciftci. "Private and Secure Data Communication: Information Theoretic Approach." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469137249.
Повний текст джерелаBauer, David Allen. "Preserving privacy with user-controlled sharing of verified information." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31676.
Повний текст джерелаCommittee Chair: Blough, Douglas; Committee Member: Ahamad, Mustaque; Committee Member: Liu, Ling; Committee Member: Riley, George; Committee Member: Yalamanchili, Sudha. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Burdon, Mark. "The conceptual and operational compatibility of data breach notification and information privacy laws." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/47512/1/Mark_Burdon_Thesis.pdf.
Повний текст джерелаBatistic, Kristina. "Privacy in Smart Parking." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-272998.
Повний текст джерелаExamensarbetet kommer att analysera den smarta parkeringslösningen som används i Frederiksbergs kommun med fokus på sekretessaspekter i olika fall för dataanvändning. Det aktuella användningsfallet kommer att analyseras med fokus på dess integritetsaspekter. Frederiksberg kommun använder en kamera monterad på en bil som registrerar parkerade bilar för att kontrollera om parkeringsavgiften har betalats eller inte. Systemet känner igen registreringsskylten ur bilden och kontrollerar i systemet om parkeringen för den typskylten har betalats eller inte om den inte har betalats, meddelar parkeringsvakten att gå till den parkerade bilen och dela ut en parkeringsbiljett. Eftersom licensskylten betraktas som personuppgifter måste detta system följa de lagliga och andra skyldigheterna för hantering av personuppgifter, dvs. den nya förordningen om europeisk allmän dataskydd. Frederiksberg kommun överväger också att använda data för sekundära ändamål, som parkeringsstatistik, input för framtida reglering, analys för att förbättra parkeringssystemet eller till och med offentliggöra uppgifterna. Denna avhandling kommer att analysera eventuella fall av sekundär användning och deras inverkan på sekretess och rekommendera skyddsåtgärder. Målet skydda medborgarnas integritet samtidigt som de ger bästa möjliga service.
Kartaev, Timur. "Assessment of Privacy-preserving Computation Techniques for Marketing Analytics." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280884.
Повний текст джерелаFör närvarande kräver ett stort antal tjänster personlig information för ana- lys, vilket orsakar integritetsproblem. I synnerhet strävar marknadsavdelning- arna efter detaljerad personlig information för en mer personlig reklamupp- levelse. Dataanonimiseringen hjälper till att bevara den enskildes integritet medan den fortfarande tillhandahåller marknadsavdelningen användarnas in- formation. Dessutom bör anonymiseringsprocessen optimeras för att hitta en balans mellan datasystemets verktyg och integritet. Dessutom gör den senaste databehandlingsregleringen, särskilt GDPR-lagen, anonymisering av data än- nu mer relevant i dag. Denna avhandling fokuserar på bedömningen av olika anonymiseringsmodeller i samband med marknadsanalys. Den utvärderar de tre vanligaste sekretessmodellerna: k-anonymitet, l-mångfald och t-närhet. In- om ramen för marknadsanalys är uppgiften att förutsäga marknadsföringska- nalens intäkter med köp i appen. Först används modellerna för att anonymisera användarnas köp. För det andra, baserat på anonymiserade inköp, förutsägas den framtida trenden för användarköp av den specifika marknadsföringskana- len. Resultaten visar att det alltid finns en avvägning mellan verktyg och sek- retess vid anonymisering av data. I första hand ger t-närhet högsta integritet. Emellertid har de anonymiserade uppgifterna med t-närheten ett helt annat in- köpsmönster än faktiska uppgifter. Å andra sidan ger k-anonymitetsmodellen den lägsta informationsförlusten och den lägsta sekretess när den tillämpas på inköpsdatasats. Slutligen, för att sammanfatta, är l-mångfalden den mest lämpade för att följa GDPR-begränsningar när anonymisering av användares inköpsdata och för förutsägelser baserade på anonymiserad data.
Wang, Xiwei. "Data Privacy Preservation in Collaborative Filtering Based Recommender Systems." UKnowledge, 2015. http://uknowledge.uky.edu/cs_etds/35.
Повний текст джерелаMiracle, Jacob M. "De-Anonymization Attack Anatomy and Analysis of Ohio Nursing Workforce Data Anonymization." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1482825210051101.
Повний текст джерелаRodhe, Ioana. "Secure and Privacy-Aware Data Collection in Wireless Sensor Networks." Doctoral thesis, Uppsala universitet, Avdelningen för datorteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-180087.
Повний текст джерелаWISENET
Söderqvist, Mikael. "Privacy concerns in Ambient Assisted Living systems for home environments." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252700.
Повний текст джерелаTill år 2060 är det uppskattat att 30% av den europeiska befolkningen kommer vara 60 år eller äldre. En åldrande befolkning kommer ge en högre belastningen på sjukvård och hemvård. För att minska belastningen och för att möjliggöra ett förlängt självständigt liv hos äldre i eget hem togs AAL-programmet fram. Med sjukvård och sjukvårds övervakning i hemmet ökar också frågeställningar kring den privata sfären och säkerhet, något som bör ha högsta prioritet. Målet med denna systematiska litteraturstudie är att identifiera de främst förekommande orsakerna till oro för påverkan av den privata sfären. I denna studie genomgick 1000 studier en trestegs inkluderingsprocess. Detta ledde fram till 30 inkluderade studier som representerar olika forskningsområden samt användarundersökningar. De inkluderade studierna kategoriseras enligt tre kategorier i relation till hur de tog upp orosområden inom privacy. Kategoriseringen fann 18 abstrakta orosområden, 12 starka orosområden och 8 lösningar till orosområden. Främst förekommande orosområden för användare var den abstrakta oron vilket kan vara ett hinder för adaptionen av AAL-system i hemmiljöer. Andra intressanta resultat inkluderar fortsatta forskningsområden såsom dataflödesrepresentation för användare, samtyckesinsamling samt avvägning mellan integritet och information.
Iwaya, Leonardo H. "Secure and Privacy-aware Data Collection and Processing in Mobile Health Systems." Licentiate thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-46982.
Повний текст джерелаInformation security and privacy are paramount to achieve high quality healthcare services, and further, to not harm individuals when providing care. With that in mind, we give special attention to the category of Mobile Health (mHealth) systems. That is, the use of mobile devices (e.g., mobile phones, sensors, PDAs) to support medical and public health. Such systems, have been particularly successful in developing countries, taking advantage of the flourishing mobile market and the need to expand the coverage of primary healthcare programs. Many mHealth initiatives, however, fail to address security and privacy issues. This, coupled with the lack of specific legislation for privacy and data protection in these countries, increases the risk of harm to individuals. The overall objective of this thesis is to enhance knowledge regarding the design of security and privacy technologies for mHealth systems. In particular, we deal with mHealth Data Collection Systems (MDCSs), which consists of mobile devices for collecting and reporting health-related data, replacing paper-based approaches for health surveys and surveillance.
Mittal, Nupur. "Data, learning and privacy in recommendation systems." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S084/document.
Повний текст джерелаRecommendation systems have gained tremendous popularity, both in academia and industry. They have evolved into many different varieties depending mostly on the techniques and ideas used in their implementation. This categorization also marks the boundary of their application domain. Regardless of the types of recommendation systems, they are complex and multi-disciplinary in nature, involving subjects like information retrieval, data cleansing and preprocessing, data mining etc. In our work, we identify three different challenges (among many possible) involved in the process of making recommendations and provide their solutions. We elaborate the challenges involved in obtaining user-demographic data, and processing it, to render it useful for making recommendations. The focus here is to make use of Online Social Networks to access publicly available user data, to help the recommendation systems. Using user-demographic data for the purpose of improving the personalized recommendations, has many other advantages, like dealing with the famous cold-start problem. It is also one of the founding pillars of hybrid recommendation systems. With the help of this work, we underline the importance of user’s publicly available information like tweets, posts, votes etc. to infer more private details about her. As the second challenge, we aim at improving the learning process of recommendation systems. Our goal is to provide a k-nearest neighbor method that deals with very large amount of datasets, surpassing billions of users. We propose a generic, fast and scalable k-NN graph construction algorithm that improves significantly the performance as compared to the state-of-the art approaches. Our idea is based on leveraging the bipartite nature of the underlying dataset, and use a preprocessing phase to reduce the number of similarity computations in later iterations. As a result, we gain a speed-up of 14 compared to other significant approaches from literature. Finally, we also consider the issue of privacy. Instead of directly viewing it under trivial recommendation systems, we analyze it on Online Social Networks. First, we reason how OSNs can be seen as a form of recommendation systems and how information dissemination is similar to broadcasting opinion/reviews in trivial recommendation systems. Following this parallelism, we identify privacy threat in information diffusion in OSNs and provide a privacy preserving algorithm for the same. Our algorithm Riposte quantifies the privacy in terms of differential privacy and with the help of experimental datasets, we demonstrate how Riposte maintains the desirable information diffusion properties of a network
Boucher, Duane Eric. "An information privacy model for primary health care facilities." Thesis, University of Fort Hare, 2013. http://hdl.handle.net/10353/d1007181.
Повний текст джерелаBanerjea-Brodeur, Nicolas Paul. "Advance passenger information passenger name record : privacy rights and security awareness." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80909.
Повний текст джерелаPassenger Name Record access permits authorities to have additional data that could identify individuals requiring more questioning prior to border control clearance. This data does not cause in itself privacy issues other than perhaps the potential retention and manipulation of information that Border Control Authorities may acquire. In essence, bilateral agreements between governments should be sought in order to protect national legislation.
The common goal of the airline industry is to ensure safe and efficient air transport. API and PNR should be viewed as formalities that can facilitate border control clearance and prevent the entrance of potentially high-risk individuals.
Harvey, Brett D. "A code of practice for practitioners in private healthcare: a privacy perspective." Thesis, Nelson Mandela Metropolitan University, 2007. http://hdl.handle.net/10948/521.
Повний текст джерелаParameswaran, Rupa. "A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/11459.
Повний текст джерелаClarke, Roger Anthony, and Roger Clarke@xamax com au. "Data Surveillance: Theory, Practice & Policy." The Australian National University. Faculty of Engineering and Information Technology, 1997. http://thesis.anu.edu.au./public/adt-ANU20031112.124602.
Повний текст джерелаErvik, Sara. "Privacy by Design applied in Practice and the Consequences for System Developers." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-251672.
Повний текст джерелаAnvändares integritet har blivit allt viktigare i takt med att mer data hanteras, inklusive känslig personlig information. Organisationer är skyldiga att ta ansvar för sina användares integritet. Det är obligatoriskt enligt lag för organisationer att hantera personlig information i enlighet med kraven definierade i direktivet Allmän Dataskyddsförordning eller General Data Protection Regulation(GDPR) på engelska. Men det kvarstår en klyfta mellan de juridiska kraven och tekniska lösningar. Inbyggd integritet eller Privacy by Design(PbD) på engelska består av principer för att utforma system med hänsyn till integritet, men metoden saknar konkreta implementationer. Denna studie undersöker hur PbD kan appliceras i ett system och hur det påverkar systemutvecklingen. Studien använder Colesky, Hoepman och Hillens tillvägagångssätt för att applicera PbD i praktiken. Med denna metod utvecklades en modell av ett system som tar hänsyn till användarnas integritet likväl systemutvecklarnas behov och systemkrav. Utvärderingen visade att systemutvecklarna var positiva till den föreslagna systemmodellen implementerad med PbD. Systemutvecklarna estimerade att den föreslagna systemmodellen skulle medföra en lätt minskning i produktiviteten men förmodade att de positiva effekterna av inbyggd integritet skulle väga upp nackdelarna.
Balla, Stefano. "Privacy-Preserving Data Mining: un approccio verticale." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17517/.
Повний текст джерелаKitkowska, Agnieszka. "Advancing Models of Privacy Decision Making : Exploring the What & How of Privacy Behaviours." Licentiate thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-69974.
Повний текст джерелаGrowing dependency on Internet-connected devices and increasing privacy risks prompted policymakers to protect individuals’ right to privacy. In Europe, the General Data Protection Regulation requires companies to provide users with adequate information about data collection and processing practices to increase privacy awareness and enable better decisions. Hence, multidisciplinary researchers aim at developing new privacy-enhancing solutions. However, to develop such solutions it is crucial to understand cognitive processes underpinning privacy decisions. This thesis objective is to investigate privacy behaviours. We identify privacy concerns affecting perceptions of privacy and examine factors influencing information sharing. We show that simplified models of behaviour are insufficient predictors of privacy decisions, and that demographic characteristic, emotion and personality affect privacy attitudes and behaviours. Based on our findings we conclude that future models of privacy and designs of privacy user interfaces must incorporate such behavioural determinants.
Åhlfeldt, Rose-Mharie. "Information Security in Distributed Healthcare : Exploring the Needs for Achieving Patient Safety and Patient Privacy." Doctoral thesis, Stockholm University, Department of Computer and Systems Sciences (together with KTH), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7407.
Повний текст джерелаIn healthcare, patient information is a critical factor. The right information at the right time is a necessity in order to provide the best possible care for a patient. Patient information must also be protected from unauthorized access in order to protect patient privacy. It is furthermore common for patients to visit more than one healthcare provider, which implies a need for cross border healthcare and continuity in the patient process.
This thesis is focused on information security in healthcare when patient information has to be managed and communicated between various healthcare actors and organizations. The work takes a practical approach with a set of investigations from different perspectives and with different professionals involved. Problems and needs have been identified, and a set of guidelines and recommendations has been suggested and developed in order to improve patient safety as well as patient privacy.
The results show that a comprehensive view of the entire area concerning patient information management between different healthcare actors is missing. Healthcare, as well as patient processes, have to be analyzed in order to gather knowledge needed for secure patient information management.
Furthermore, the results clearly show that there are deficiencies both at the technical and the administrative level of security in all investigated healthcare organizations.
The main contribution areas are: an increased understanding of information security by elaborating on the administrative part of information security, the identification of information security problems and needs in cross border healthcare, and a set of guidelines and recommendations in order to advance information security measures in healthcare.
Meddeoda, Gedara Kavindra Kulathilake. "Design for Addressing Data Privacy Issues in Legacy Enterprise Application Integration." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-74245.
Повний текст джерелаMenfors, Martina, and Felicia Fernstedt. "Geotagging in social media : exploring the privacy paradox." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-8685.
Повний текст джерелаGhafghazi, Hamidreza. "Privacy-Preserving Location-Aware Data Availability and Access Authorization in Public Safety Broadband Networks." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36006.
Повний текст джерелаIlesanmi, Olufemi Olajide. "Privacy in RFID Transit Systems : A case study of SL - Storstockholms Lokaltrafik." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177584.
Повний текст джерелаDemirsoy, Delil, and Erik Holm. "En studie om Big data och personlig integritet : Vad vet studenter om lagring av deras personliga uppgifter?" Thesis, Högskolan Väst, Avd för informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-15395.
Повний текст джерелаThis research study examines students' knowledge of personal data stored by institutions of higher education as well as, whether there are differences between the genders regarding the knowledge and the management of this stored data. This is connected to the expanding storage of data and the use of it through Big data within the organisations where it was shown to have impact on the personal integrity. Previous studies report that there is a knowledge gap within society regarding the information on what is stored. In addition to this, research showed that there are differences between genders about the knowledge and control of their personal data. Therefore, this study focuses on students' knowledge of what higher education institutions store about them as well as whether there are differences between genders. This study applies a quantitative research method in the form of electronic questionnaires for data collection. These questionnaires were handed out to students which contained questions about students' knowledge and views about their institution's storage and management of their personal data. A total of 151 people participated in this study, where 126 of the participants stated that they were students. Moreover. this study includes some elements of qualitative research methods where some of the questions in the electronic questionnaires could be answered in free text. The qualitative and quantitative methods were later analyzed and compared to Petrionio's CPM-theory and its five principles regarding the personal integrity. The result of the study showed that students' have a vague form of knowledge regarding the data stored by institutions of higher education. The research also indicated that there are differences between the sexes in the handling of personal data. However, our findings show that the lack of dropout analysis makes the mentioned findings quite difficult to be fully verified. The result has shown that several students did not feel secure when organizations within higher educational institutions stored personal data about them. This is because they feel that their knowledge on what is being stored is insufficient which consequently led them to feel a lacking control about their own personal integrity. Thus, results showed that most people think that the educational institutions should provide more specific information about the personal data that they store about them.
Pagès, Billai Linn. "Designing a Comprehensive Privacy Policy : A qualitative comparative study." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-293357.
Повний текст джерелаSociala medieplattformar måste tillhandahålla en integritet eller datapolicy för användare av sina tjänster och dessa juridiska handlingar tenderar att visa egenskaper av dålig användarupplevelse. En majoritet av användarna har svårigheter att förstå integritetspolicyn på grund av den långa och legalistiska karaktären hos dessa dokument, vilket i sin tur resulterar i färre läsare. Genom att undersöka användarnas attityder till integritetspolicyn på den social medieplattformen Instagram, var en prototyp av en alternativ integritetspolicy som syftar till att förbättra användarnas engagemang utformad. Double Diamond-modellen användes för att undersöka prototypens designprocess. Detta inkluderade en första litteraturstudie tillsammans med kvalitativa intervjuer. Resultatet från intervjuerna visade att deltagarna sällan läste integritetspolicys på grund av deras långa och oläsliga natur och att en mer begriplig policy skulle vara till hjälp. Den slutliga utformningen av prototypen skapades iterativt efter återkoppling från användartester. Den nya prototypen visade sig vara lätt att förstå och snabbare för att navigera än den ursprungliga datapolicyn. Majoriteten av användarna uttryckte också en preferens för den alternativa prototypen. De intressanta resultaten från denna preliminära studie kan vidtas ytterligare i en fullskalig studie med ett större antal deltagare och representation från olika åldersgrupper.
Jellen, Isabel. "Towards Security and Privacy in Networked Medical Devices and Electronic Healthcare Systems." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2141.
Повний текст джерелаKatirai, Hooman. "A theory and toolkit for the mathematics of privacy : methods for anonymizing data while minimizing information loss." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34526.
Повний текст джерелаIncludes bibliographical references (leaves 85-86).
Privacy laws are an important facet of our society. But they can also serve as formidable barriers to medical research. The same laws that prevent casual disclosure of medical data have also made it difficult for researchers to access the information they need to conduct research into the causes of disease. But it is possible to overcome some of these legal barriers through technology. The US law known as HIPAA, for example, allows medical records to be released to researchers without patient consent if the records are provably anonymized prior to their disclosure. It is not enough for records to be seemingly anonymous. For example, one researcher estimates that 87.1% of the US population can be uniquely identified by the combination of their zip, gender, and date of birth - fields that most people would consider anonymous. One promising technique for provably anonymizing records is called k-anonymity. It modifies each record so that it matches k other individuals in a population - where k is an arbitrary parameter. This is achieved by, for example, changing specific information such as a date of birth, to a less specific counterpart such as a year of birth.
(cont.) Previous studies have shown that achieving k-anonymity while minimizing information loss is an NP-hard problem; thus a brute force search is out of the question for most real world data sets. In this thesis, we present an open source Java toolkit that seeks to anonymize data while minimizing information loss. It uses an optimization framework and methods typically used to attack NP-hard problems including greedy search and clustering strategies. To test the toolkit a number of previously unpublished algorithms and information loss metrics have been implemented. These algorithms and measures are then empirically evaluated using a data set consisting of 1000 real patient medical records taken from a local hospital. The theoretical contributions of this work include: (1) A new threat model for privacy - that allows an adversary's capabilities to be modeled using a formalism called a virtual attack database. (2) Rationally defensible information loss measures - we show that previously published information loss measures are difficult to defend because they fall prey to what is known as the "weighted indexing problem." To remedy this problem we propose a number of information-loss measures that are in principle more attractive than previously published measures.
(cont.) (3) Shown that suppression and generalization - two concepts that were previously thought to be distinct - are in fact the same thing; insofar as each generalization can be represented by a suppression and vice versa. (4) We show that Domain Generalization Hierarchies can be harvested to assist the construction of a Bayesian network to measure information loss. (5) A database can be thought of as a sub-sample of a population. We outline a technique that allows one to predict k-anonymity in a population. This allows us, under some conditions, to release records that match fewer than k individuals in a database while still achieving k-anonymity against an adversary according to some probability and confidence interval. While we have chosen to focus our thesis on the anonymization of medical records, our methodologies, toolkit and command line tools are equally applicable to any tabular data such as the data one finds in relational databases - the most common type of database today.
by Hooman Katirai.
S.M.
Burkhart, Martin [Verfasser]. "Enabling Collaborative Network Security with Privacy-Preserving Data Aggregation / Martin Burkhart." Aachen : Shaker, 2011. http://d-nb.info/1071528394/34.
Повний текст джерелаBernal, Paul Alexander. "Do deficiencies in data privacy threaten our autonomy and, if so, can informational privacy rights meet this threat?" Thesis, London School of Economics and Political Science (University of London), 2011. http://etheses.lse.ac.uk/321/.
Повний текст джерелаSiganto, Jean Josephine. "Transparent, balanced and vigorous: The exercise of the Australian Privacy Commissioner's powers in relation to National Privacy Principle 4." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/83792/4/Jean_Siganto_Thesis.pdf.
Повний текст джерела