Sökning: "Data privacy"

Visar resultat 16 - 20 av 245 avhandlingar innehållade orden Data privacy.

  1. 16. Usable privacy for digital transactions : Exploring the usability aspects of three privacy enhancing mechanisms

    Författare :Julio Angulo; John Sören Pettersson; Simone Fischer-Hübner; Erik Wästlund; Karlstads universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Privacy-Enhancing Technologies; usability; usable privacy; mental models; mobile devices; security; digital transactions; e-commerce; User Interfaces; Information Systems; Informatik;

    Sammanfattning : The amount of personal identifiable information that people distribute over different online services has grown rapidly and considerably over the last decades. This has led to increased probabilities for identity theft, profiling and linkability attacks, which can in turn not only result in a threat to people’s personal dignity, finances, and many other aspects of their lives, but also to societies in general. LÄS MER

  3. 17. Privacy in the Age of Artificial Intelligence

    Författare :Aristide Tossou; Chalmers tekniska högskola; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; sequential decision problem; multi-armed bandit; differential privacy;

    Sammanfattning : An increasing number of people are using the Internet in their daily life. Indeed, more than 40% of the world population have access to the Internet, while Facebook (one of the top social network on the web) is actively used by more than 1.3 billion users each day (Statista 2017). LÄS MER

  4. 18. Our Humanity Exposed : Predictive Modelling in a Legal Context

    Författare :Stanley Greenstein; Peter Wahlgren; Dan Svantesson; Stockholms universitet; []
    Nyckelord :SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; predictive modelling; predictive analytics; profiling; big data; algorithm; surveillance; privacy; autonomy; identity; digital identity; data privacy; human rights; data protection; European Convention on Human Rights; Data Protection Directive; General Data Protection Regulation GDPR ; empowerment; Law and Information Technology; rättsinformatik;

    Sammanfattning : This thesis examines predictive modelling from the legal perspective. Predictive modelling is a technology based on applied statistics, mathematics, machine learning and artificial intelligence that uses algorithms to analyse big data collections, and identify patterns that are invisible to human beings. LÄS MER

  5. 19. Server-Aided Privacy-Preserving Proximity Testing

    Författare :Ivan Oleinikov; Chalmers tekniska högskola; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; active security; MPC; privacy; secure proximity testing;

    Sammanfattning : Proximity testing is at the core of many Location-Based online Services (LBS) which we use in our daily lives to order taxis, find places of interest nearby, connect with people. Currently, most such services expect a user to submit his location to them and trust the LBS not to abuse this information, and use it only to provide the service. LÄS MER

  7. 20. Towards Scalable Machine Learning with Privacy Protection

    Författare :Dominik Fay; Mikael Johansson; Tobias J. Oechtering; Jens Sjölund; Antti Honkela; KTH; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Privacy; Differential Privacy; Dimensionality Reduction; Image Segmentation; Hyperparameter Selection; Adaptive Optimization; Privacy Amplification; Importance Sampling; Maskininlärning; Dataskydd; Differentiell Integritet; Dimensionsreducering; Bildsegmentering; Hyperparameterurval; Adaptiv Optimering; Integritetsförstärkning; Importance Sampling; Datalogi; Computer Science; Informations- och kommunikationsteknik; Information and Communication Technology;

    Sammanfattning : The increasing size and complexity of datasets have accelerated the development of machine learning models and exposed the need for more scalable solutions. This thesis explores challenges associated with large-scale machine learning under data privacy constraints. LÄS MER