Protecting User Privacy with Social Media Data and Mining
نام عام مواد
[Thesis]
نام نخستين پديدآور
Beigi, Ghazaleh
نام ساير پديدآوران
Liu, Huan
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
Arizona State University
تاریخ نشرو بخش و غیره
2020
مشخصات ظاهری
نام خاص و کميت اثر
169
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
Ph.D.
کسي که مدرک را اعطا کرده
Arizona State University
امتياز متن
2020
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
The pervasive use of the Web has connected billions of people all around the globe and enabled them to obtain information at their fingertips. This results in tremendous amounts of user-generated data which makes users traceable and vulnerable to privacy leakage attacks. In general, there are two types of privacy leakage attacks for user-generated data, i.e., identity disclosure and private-attribute disclosure attacks. These attacks put users at potential risks ranging from persecution by governments to targeted frauds. Therefore, it is necessary for users to be able to safeguard their privacy without leaving their unnecessary traces of online activities. However, privacy protection comes at the cost of utility loss defined as the loss in quality of personalized services users receive. The reason is that this information of traces is crucial for online vendors to provide personalized services and a lack of it would result in deteriorating utility. This leads to a dilemma of privacy and utility.
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
Artificial intelligence
موضوع مستند نشده
Computer science
موضوع مستند نشده
Machine learning
موضوع مستند نشده
Privacy protection
موضوع مستند نشده
Social media mining
موضوع مستند نشده
User behavioral modeling
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )