An Effective Anonymization Technique for Privacy Preserving Data Publishing in Inter Cloud Infrastructure

Abstract

The widespread use of digital devices and information systems has made data privacy newlinean unavoidable concern. Data is collected enormously in many places, such as schools, newlinegovernment agencies, and e-commerce websites. The data gathered from these systems newlineincludes various Personally Identifiable Information (PII). The PII contains sensitive and newlinenon-sensitive attribute specific to an individual. Some examples of sensitive PII are newlinenames, financial information, driving license, medical records, relationship, and marital newlinestatus. Non-sensitive PII (quasi-identifiers) are easily accessible from public sources, newlineincluding zip code, race, gender, and date of birth. newlinePII is exchanged or published to a third party using cloud infrastructure to perform newlinevarious analysis, conduct research, and make critical decisions. Cloud computing offers newlinevaluable services like enhanced collaboration, accessibility, and limitless storage. Using newlineinter-cloud infrastructure, when data is stored in partitions, it prevents an intruder from newlineaccessing complete data. Apart from constructive usage of the published data, there newlinemay be an intruder who uses the data and cause privacy attacks. People s privacy is at newlinerisk when the PII data is published, as they have no control over the data movement. newlineTherefore, protecting the individual s privacy before publishing is vital to avoid specific newlinedisclosures and threats. This led to the development of privacy preserving data publishing newlinetechniques. newlinePrivacy Preserving Data Publishing (PPDP) is a suite of algorithms, frameworks, newlineand prototypes developed to prevent disclosures. Data anonymization is one method for newlineachieving PPDP. Data masking, data disruption, and data encryption are other strategies. newlineData anonymization is desirable since it reduces information loss and allows published newlinedata to be used more efficiently. Other methods involve using synthetic data, key management, newlineand total data concealing before publishing. Due to this, the data utilization newlinemay not be optimal. newline newline