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|Title:||Data Cleaning with Minimal Information Disclosure|
|Keywords:||Data cleaning;Data management;Data privacy;Information theory|
|Abstract:||Businesses analyze large datasets in order to extract valuable insights from the data. Unfortunately, most real datasets contain errors that need to be corrected before any analysis. Businesses can utilize various data cleaning systems and algorithms to automate the correction of data errors. Many systems correct the data errors by using information present within the dirty dataset itself. Some also incorporate user feedback in order to validate the quality of the suggested data corrections. However, users are not always available for feedback. Hence, some systems rely on clean data sources to help with the data cleaning process. This involves comparing records between the dirty dataset and the clean dataset in order to detect high quality fixes for the erroneous data. Every record in the dirty dataset is compared with every record in the clean dataset in order to find similar records. The values of the records in the clean dataset can be used to correct the values of the erroneous records in the dirty dataset. Realistically, comparing records across two datasets may not be possible due to privacy reasons. For example, there are laws to restrict the free movement of personal data. Additionally, different records within a dataset may have different privacy requirements. Existing data cleaning systems do not factor in these privacy requirements on the respective datasets. This motivates the need for privacy aware data cleaning systems. In this thesis, we examine the role of privacy in the data cleaning process. We present a novel data cleaning framework that supports the cooperation between the clean and the dirty datasets such that the clean dataset discloses a minimal amount of information and the dirty dataset uses this information to (maximally) clean its data. We investigate the tradeoff between information disclosure and data cleaning utility, modelling this tradeoff as a multi-objective optimization problem within our framework. We propose four optimization functions to solve our optimization problem. Finally, we perform extensive experiments on datasets containing up to 3 million records by varying parameters such as the error rate of the dataset, the size of the dataset, the number of constraints on the dataset, etc and measure the impact on accuracy and performance for those parameters. Our results demonstrate that disclosing a larger amount of information within the clean dataset helps in cleaning the dirty dataset to a larger extent. We find that with 80% information disclosure (relative to the weighted optimization function), we are able to achieve a precision of 91% and a recall of 85%. We also compare our algorithms against each other to discover which ones produce better data repairs and which ones take longer to find repairs. We incorporate ideas from Barone et al. into our framework and show that our approach is 30% faster, but 7% worse for precision. We conclude that our data cleaning framework can be applied to real-world scenarios where controlling the amount of information disclosed is important.|
|Appears in Collections:||Open Access Dissertations and Theses|
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|gairola_dhruv_201507_msc_computer_science.pdf||Thesis||1.47 MB||Adobe PDF||View/Open|
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