Privacy for Big Data
Jawwad Ahmed Shamsi, Muhammad Ali Khojaye · 2021
Big data analytics may yield to privacy violations such that confidential data of users may be exposed either intentionally or unintentionally. There are several possibilities of privacy violation. Governments may track people to enhance national security, whereas service providers may collect information to improve business and recommend related products. In addition, attackers can purposely launch re-identification attacks through external sources. These could include correlation attack, arbitrary identification attack, and target identification attack. All these threats and attacks utilize different data sources to expose a person&s;s confidential information. Further, privacy could be violated in case of data breach such that data is stolen using security attacks. Privacy could be enhanced using several techniques such as K-anonymity, L-diversity, T-closeness, and Differential Privacy. Strengthening laws and regulations, enforcing security, and improving cooperation between civil society and Government can also limit privacy violations. The chapter provides an overview of privacy issues and challenges in the context of big data and highlights significant guidelines which can be used to strengthen privacy of the users in big data systems.