Privacy Threat Modeling

What is Privacy Threat Modeling? 👀

Privacy Threat Modeling is a proactive methodology that helps identify and mitigate potential privacy risks and violations within a system or dataset.

As the provided statistical disclosure controls and other privacy protection mechanism shows, they are vulnerable to many privacy attacks.

So even if we utilized any of the previous de-identification methods, we must conduct a privacy threat modeling assessment to make sure there are no leaks from the datasets that we share we disclose.

Why do we need privacy threat modeling? 🔍

Privacy threat modeling is essential because it allows us to anticipate and address potential privacy risks in a proactive manner.

Introduction to Threat Modeling in Security: 🛡️

Threat modeling is a systematic approach used in security to identify potential threats and vulnerabilities in a system or application.

One commonly used framework for security threat modeling is the STRIDE model, which categorizes threats into six types: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege.

By using the STRIDE model, security professionals can assess and prioritize threats to design appropriate security controls.

Introduction to Threat Modeling in Privacy: 🙈

Similarly, in the context of privacy, threat modeling focuses on identifying and mitigating risks that could lead to the compromise of sensitive information. The LINDDUN Framework is a well-known approach for privacy threat modeling.

It identifies seven privacy threat categories: Linkability, Identifiability, Non-Repudiation, Detectability, Disclosure of Information, Unawareness, and Non-Compliance.

Privacy threat modeling with the LINDDUN framework helps in understanding how individuals’ right to privacy might be violated within the target system.

What are threat categories? 👀

Threat modeling involves identifying and categorizing potential threats to a system or application. The LINDDUN privacy threat types help you detect potential privacy concerns in a systematic and structured way.

This knowledge is structured according to the 7 main threat types captured in the acronym LINDDUN.

• Linkability

• Identifying

• Non Repudation

• Detecting

• Data Disclosure

• Unawareness

• Non Compliance

Identifying Threat: 🕵️‍♂️

• Focus: Techniques like generalization, randomization, and k-anonymity have limitations and can be susceptible to Attribute Disclosure, Linkage Attacks, and Background Knowledge Attacks.

• Risks: Data breaches, unintentional disclosures, and inference attacks pose significant risks to individuals’ privacy.

• Mitigation: You can employ more secure output privacy measures like differential privacy. Alternatively, you can opt for stricter traditional statistical disclosure control and de-identification techniques.

Linking Threat: 🧩

• Focus: The linking threat involves associating data items or user actions to gain insights about individuals or groups. It entails connecting the dots between different data points to reveal sensitive information.

• Risks: Data aggregation, cross-referencing, and pattern analysis pose risks of identification and privacy violations.

• Mitigation: Implementing strict de-identification techniques, such as Dissociation to break the link between sensitive information and individuals. Applying K Anonymity ensures that each record is indistinguishable from at least “K-1” other records, providing an added layer of privacy protection. Synthetic Data Generation can be used to share data for research and development purposes without revealing actual personal information.

Detecting Threat Category: 👥

• Focus: The detecting threat category revolves around inferring sensitive information about individuals in the dataset without directly accessing the data.

• Risks: Pattern recognition, behavior analysis, and profiling can lead to unintended privacy breaches.

• Mitigation: To protect individual privacy while maintaining data accuracy, employing Differential Privacy to add noise to query results is an effective strategy. Applying K Anonymity ensures that each record is indistinguishable from at least “K-1” other records, providing an added layer of privacy protection. Synthetic Data Generation can be used to share data for research and development purposes without revealing actual personal information.