## 5 A Case Study of Organisational Data-Sharing Business Model Challenges
### Case Study: Facebook and Cambridge Analytica Scandal
#### 5.1.3 C2C: Cambridge Analytica and Facebook User Friends' Data Analysis
In the US presidential election context, CA used their data to analyse users' psychometric profiles to identify the most vulnerable individuals. Their aim was to influence the individuals' political beliefs, so rather than focusing on the majority of the voters in the election, they focused on minorities who shared common attributes or characteristics. For example, CA identifies users who are described as \"hesitant or worried\". Another way CA micro-targeted political ads was to identify the groups of users with specific conditions--for example, running political ads appealing to groups who own firearms. Identified users with similar attributes were effectively categorised as members of a specific psychological or behavioural group. Identification and revelation grouping of users based on shared characteristics, conditions, or interests indicates a form of membership disclosure. In the context of privacy disclosure from third-party organisations, the main issue is related to the primary organisation's management of the data. As CA and Kogan collected and processed data in such ways that led to privacy disclosure, Facebook's management of the data was poor. It was only after the privacy breach that Facebook updated their policies and tightened the security of its user's data. Privacy frameworks such as NIST and Five Safes introduce practices that can overcome privacy issues, including membership disclosure. To ensure the security and privacy of individuals' data, the NIST privacy framework provides the control function. This function contains three categories that provide data procedures, data management, and data authority. All the sub-categories are inclusive and applicable to resolve the privacy disclosure risk in B2B. 1. The initial sub-categories (CT.PO-P1-P4) discuss the importance of developing procedures and policies to keep an authority on data processing. The significance of these procedures and policies is to empower the primary organisations to exercise their control of the data by maintaining and establishing practices that ensure the respect of the individual's privacy rights and stay aligned with privacy regulations. 2. The sub-categories of data processing management (CT.DM-P1-P10) provide valuable measures for organisations to effectively manage data while upholding privacy regulations. These measures provide management and control to the primary organisation for accessibility, data transmission, data alteration, data deletion, data destruction, standardisation, and audit practices that respect the privacy preferences of the individual's data. 3. The last category, disassociated processing, contains five sub-categories (CT.DP-P1-P5) that focus on creating data-processing solutions to increase the disassociability of data with the individual's identity. The goal of this category is to develop processes that can limit the processing of data or events to associate with an individual--for example, by using privacy-preserving models such as differential privacy [61] to identify individuals or limiting the likability and attributes referencing an individual's behaviour or activities. One privacy risk is membership disclosure, where an individual's affiliation with certain groups is revealed through data processing. Specifically, this issue resolves around the targeted manipulation of vulnerable users who have particular characteristics or conditions and are associated with specific groups. The Five Safes privacy framework provides a guideline to mitigate the risks associated with membership disclosure. The \"Safe Outputs\" is a principle that provides an approach to ensure that data processing and analysis are conducted in a manner that respects the individual's privacy rights and minimises the privacy risks. 1. Before the data analysis is published, the final stages, according to the Five Safes privacy frameworks, ensure there are strict privacy controls implemented in the outputs of data analysis, so the statistical results are non-disclosive.