Enhanced Privacy Preservation in Facebook Databases Using an Combined Approach with K-Member Fuzzy Clustering and Lyrebird Optimization Algorithm
Keywords:
Facebook, Lyrebird Optimization Algorithm, Anonymization Technique, Fuzzy Clustering Algorithm, Digital Forensics.Abstract
Facebook's rapid growth has led to the collection of vast personal data, including age, location, occupation, and contact information, which poses significant privacy risks despite its utility in law enforcement and forensic investigations. The primary challenge is balancing the need for forensic access with protecting user privacy, ensuring that shared data does not allow for individual identification. Traditional anonymisation strategies like K-Anonymity (KA), l-diversity (LD), and t-closeness (TC) aim to safeguard personal data by removing or altering identifying information. However, these methods often prove inadequate, leaving data exposed to attribute and link disclosures, similarity attacks, and resulting in considerable information loss. This study introduces a more efficient anonymisation technique that combines K-member fuzzy clustering with the Modified Lyrebird Optimisation Algorithm (KFCMLOA). An Enhanced K-member version of the fuzzy c-means algorithm is first used to form balanced clusters, ensuring that each cluster has a minimum of K members. These clusters are then further refined and the data is anonymised using the Lyrebird Optimisation Algorithm (LOA). This discovery is important because it can protect anonymised Facebook databases from similarity attacks, identity, attribute, and link leaks, while reducing information loss.
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