Master Degree / Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/11147/3008

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Author: search.filters.author.Ayav, TolgaSubject: search.filters.subject.Data mining

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  • Master Thesis
    An Exact Approach With Minimum Side-Effects for Association Rule Hiding
    (Izmir Institute of Technology, 2014) Leloğlu, Engin; Ayav, Tolga
    Concealing sensitive relationships before sharing a database is of utmost importance in many circumstances. This implies to hide the frequent itemsets corresponding to sensitive association rules by removing some items of the database. Research efforts generally aim at finding out more effective methods in terms of convenience, execution time and side-effect. This paper presents a practical approach for hiding sensitive patterns while allowing as much nonsensitive patterns as possible in the sanitized database. We model the itemset hiding problem as integer programming whereas the objective coefficients allow finding out a solution with minimum loss of nonsensitive itemsets. We evaluate our method using three real datasets from FIMI repository and compared the results with previous exact solution and the heuristic study whose procedures are imposed by new approach. The results show that information loss is dramatically minimized without sacrificing so many modifications on databases.
  • Master Thesis
    Parallelization of a Novel Frequent Itemset Hiding Algorithm on a Cpu-Gpu Platform
    (Izmir Institute of Technology, 2014) Heye, Samuel Bacha; Ayav, Tolga; Ayav, Tolga
    Data mining is used to extract useful information from large data. But the organizations which mine the data might not be the owner of the data. So, before the owners can make their data accessible for data mining they want to make sure that no sensitive information can be mined from the released data whose discovery by others might harm them. Itemset hiding is one mechanism to prevent the disclosure of sensitive itemsets. In this thesis, a new integer programing based itemset hiding algorithm was developed and a mechanism to speed up the computation time of its implementation was proposed by using parallel computation on Graphical Processing Units (GPUs).