Kullanilan iot cihazlarinda kullanici gizliliğimakine ve derin öğrenme yaklaşimlar

The swift expansion of Internet of Things (IoT) technology has sparked concerns regarding the privacy of users, since these devices often collect and transmit vast amounts of personal information. To address these issues, this thesis will look at the use of machine and deep learning technologies to improve user privacy on IoT devices. First, the research will examine the existing state of user privacy on IoT devices, as well as the issues associated with protecting privacy. This will include a discussion of the many types of data gathered and communicated by IoT devices, as well as the numerous ways in which this data might be exploited or hacked. The research will also look at current legislative frameworks and best practices in the sector for preserving user privacy on IoT devices. The thesis will then investigate the application of machine learning approaches to improve user privacy on IoT devices. This will take a look at the many machine learning techniques that may be used for this, such as decision tree algorithms and ANNs. The research will also look at the possible benefits and drawbacks of utilizing these algorithms for privacy protection, such as the trade-offs between privacy and other objectives like performance or accuracy. Besides machine learning, the project will look into the use of deep learning technologies for improving user privacy on IoT devices. Deep learning models, a specific category of machine learning techniques, have demonstrated significant potential across various applications. The research will examine the potential benefits and challenges of applying deep learning algorithms for privacy protection on IoT devices, as well as the present related works in this field. Finally, the dissertation will conclude with a discussion of the potential future direction of research in this area, including the potential for integrating machine and deep learning approaches with other privacy-enhancing technologies and the potential for additional regulatory or industry-led efforts to improve user privacy on IoT devices. This dissertation intends to offer a complete assessment of the present status of user privacy on IoT devices, as well as the possibilities for enhancing privacy via the application of machine and deep learning technologies. The study intends to contribute to continuing efforts to secure user privacy in the rapidly developing realm of IoT by addressing these challenges.