Anonymization is a practical solution for preserving user's privacy in data publishing. Data
owners such as hospitals, banks, social network (SN) service providers, and insurance …

Data are naturally collected in their raw state and must undergo a series of preprocessing
steps to obtain data in their input state for Artificial Intelligence (AI) and other applications …

Errors are prevalent in time series data, which is particularly common in the industrial field.
Data with errors could not be stored in the database, which results in the loss of data assets …

Web information system (WIS) is frequently-used and indispensable in daily social life. WIS
provides information services in many scenarios, such as electronic commerce …

Data cleaning is one of the most important tasks in data analysis processes. One of the
perennial challenges in data analytics is the detection and handling of non-valid data …

The research on fair machine learning (ML) has been growing due to the high demand for
unbiased and fair ML models for objective decision-making. Most of this research has been …

Data cleaning is a pervasive problem for organizations as they try to reap value from their
data. Recent advances in networking and cloud computing technology have fueled a new …

Functional Dependencies define attribute relationships based on syntactic equality, and
when used in data cleaning, they erroneously label syntactically different but semantically …

Data quality plays a key role in big data management today. With the explosive growth of
data from a variety of sources, the quality of data is faced with multiple problems. Motivated …

Using techniques employing smooth sensitivity, we develop a method for-nearest neighbor
missing data imputation with differential privacy. This requires bounding the number of data …