Distributed Differential Privacy and Applications

Recent growth in the size and scope of databases has resulted in more research into making productive use of this data. Unfortunately, a significant stumbling block which remains is protecting the privacy of the individuals that populate these datasets. As people spend more time connected to the Internet, and conduct more of their daily lives online, privacy becomes a more important consideration, just as the data becomes more useful for researchers, companies, and individuals. As a result, plenty of important information remains locked down and unavailable to honest researchers today, due to fears that data leakages will harm individuals. Recent research in differential privacy opens a promising pathway to guarantee individual privacy while simultaneously making use of the data to answer useful queries. Differential privacy is a theory that provides provable information theoretic guarantees on what any answer may reveal about any single individual in the database. This approach has resulted in a flurry of recent research, presenting novel algorithms that can compute a rich class of computations in this setting. In this dissertation, we focus on some real world challenges that arise when trying to provide differential privacy guarantees in the real world. We design and build runtimes that achieve the mathematical differential privacy guarantee in the face of three real world challenges: securing the runtimes against adversaries, enabling readers to verify that the answers are accurate, and dealing with data distributed across multiple domains

A User-driven Annotation Framework for Scientific Data

Annotations play an increasingly crucial role in scientific exploration and discovery, as the amount of data and the level of collaboration among scientists increases. There are many systems today focusing on annotation management, querying, and propagation. Although all such systems are implemented to take user input (i.e., the annotations themselves), very few systems are user-driven, taking into account user preferences on how annotations should be propagated and applied over data. In this thesis, we propose to treat annotations as first-class citizens for scientific data by introducing a user-driven, view-based annotation framework. Under this framework, we try to resolve two critical questions: Firstly, how do we support annotations that are scalable both from a system point of view and also from a user point of view? Secondly, how do we support annotation queries both from an annotator point of view and a user point of view, in an efficient and accurate way? To address these challenges, we propose the VIew-base annotation Propagation (ViP) framework to empower users to express their preferences over the time semantics of annotations and over the network semantics of annotations, and define three query types for annotations. To efficiently support such novel functionality, ViP utilizes database views and introduces new annotation caching techniques. The use of views also brings a more compact representation of annotations, making our system easier to scale. Through an extensive experimental study on a real system (with both synthetic and real data), we show that the ViP framework can seamlessly introduce user-driven annotation propagation semantics while at the same time significantly improving the performance (in terms of query execution time) over the current state of the art

Contributions to privacy protection for ubiquitous computing

El desenvolupament de noves tecnologies ha introduït el concepte de Computació Ubiqua, a on els objectes que ens envolten poden tenir processadors integrats i establir la comunicació amb altres sistemes, amb la finalitat d'oferir serveis personalitzats per ajudar-nos amb les nostres tasques habituals. No obstant això, a causa de que és possible tenir ordinadors en gairebé qualsevol lloc o objecte, això ha obert noves discussions sobre temes tals com la privadesa i la seguretat, considerats des de diferents punts de vista, com el desenvolupaments jurídics, socials, econòmics i tecnològics, amb una importància cada vegada major al món actual. En aquesta tesi discutim i analitzem algunes de les principals qüestions de seguretat i privadesa a les tecnologies actuals, tals com a telèfons intel·ligents, dispositius RFID o ciutats intel·ligents, i proposem alguns protocols per fer front a aquests temes garantint la privadesa dels usuaris a tot moment.El desarrollo de nuevas tecnologías ha introducido el concepto de Computación Ubicua , en donde los objetos que nos rodean pueden tener procesadores integrados y establecer la comunicación con otros sistemas, con el fin de ofrecer servicios personalizados para ayudarnos con nuestras tareas habituales. Sin embargo, debido a que es posible tener ordenadores en casi cualquier lugar u objeto, esto ha abierto nuevas discusiones sobre temas tales como la privacidad y la seguridad, considerado desde diferentes puntos de vista, como el desarrollos jurídicos, sociales, económicos y tecnológicos, con una importancia cada vez mayor en el mundo actual. En esta tesis discutimos y analizamos algunas de las principales cuestiones de seguridad y privacidad en las tecnologías actuales, tales como teléfonos inteligentes, dispositivos RFID o ciudades inteligentes, y proponemos algunos protocolos para hacer frente a estos temas garantizando la privacidad de los usuarios en todo momento.The development of new technologies has introduced the concept of Ubiquitous Computing, whereby the objects around us can have an embedded computer and establish communications with each other, in order to provide personalized services to assist with our tasks. However, because it is possible to have computers almost anywhere and within any object, this has opened up new discussions on issues such as privacy and security, considered from many different views, such as the legal, social, economic and technological development perspectives, all taking an increasingly significant importance in today’s world. In this dissertation we discuss and analyze some of the main privacy and security issues in current technologies, such as smartphones, RFIDs or smart cities, and we propose some protocols in order to face these issues guarantying users' privacy anytime

Scalable discovery of networked data : Algorithms, Infrastructure, Applications

Harmelen, F.A.H. van [Promotor]Siebes, R.M. [Copromotor

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI