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Data Poisoning Attacks on Linked Data with Graph Regularization

Abstract Social media has become the norm of everyone for communication. The usage of social media has increased exponentially in the last decade. The myriads of Social media services such as Facebook, Twitter, Snapchat, and Instagram etc allow people to connect with their friends, and followers freely. The attackers who try to take advantage of this situation has also increased at an exponential rate. Every social media service has its own recommender systems and user profiling algorithms. These algorithms use users current information to make different recommendations. Often the data that is formed from social media services is Linked data as each item/user is usually linked with other users/items. Recommender systems due to their ubiquitous and p... (more)
Created Date 2019
Contributor Magham, Venkatesh (Author) / Liu, Huan (Advisor) / Wu, Liang (Committee member) / Amor, Hani Ben (Committee member) / Arizona State University (Publisher)
Subject Computer science / Information science / Collaborative filtering / Data poisoning attacks / Graph laplacian / Graph regularization / Linked data / Matrix factorization
Type Masters Thesis
Extent 41 pages
Language English
Note Masters Thesis Computer Science 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis