Data Privacy: De-Identification Techniques
DEVELOPING AND CONNECTING ISSA CYBERSECURITY LEADERS GLOBALLY Data Privacy: De-Identification Techniques By Ulf Mattsson – ISSA member, New York Chapter This article discusses emerging data privacy techniques, standards, and examples of applications implementing different use cases of de-identification techniques. We will discuss different attack scenarios and practical balances between privacy requirements and operational requirements. Abstract The data privacy landscape is changing. There is a need for privacy models in the current landscape of the increas- ing numbers of privacy regulations and privacy breaches. Privacy methods use models and it is important to have a common language when defining privacy rules. This article will discuss practical recommendations to find the right practical balance between compli- ance, security, privacy, and operational requirements for each type of data and business use case. Figure 1 – Forrester’s global map of privacy rights and regulations [4] ensitive data can be exposed to internal users, partners, California Customer Privacy Act (CCPA) is a wake-up call, and attackers. Different data protection techniques can addressing identification of individuals via data inference provide a balance between protection and transparen- through a broader range of PII attributes. CCPA defines Scy to business processes. The requirements are different for personal information as information that identifies, relates systems that are operational, analytical, or test/development to, describes, is reasonably capable of being associated with, as illustrated by some practical examples in this article. or could reasonably be linked, directly or indirectly, with a We will discuss different aspects of various data privacy tech- particular consumer or household such as a real name, alias, niques, including data truthfulness, applicability to different postal address, and unique personal identifier [1].
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