Reference:
The most important consideration to ensure privacy when using big data analytics is C. Transparency about the data being collected.
A comprehensive explanation is:
Big data analytics involves the processing of large and complex data sets to extract valuable insights and patterns that can support decision making, innovation, and optimization. However, big data analytics also poses significant challenges and risks for the privacy of individuals and groups whose data is collected, stored, analyzed, and shared. Therefore, it is essential to adopt appropriate measures and principles to protect the privacy of big data while still enabling its beneficial use.
One of the key measures and principles for ensuring privacy when using big data analytics is transparency. Transparency means that the data collectors and processors inform the data subjects (the individuals or groups whose data is involved) about what data is being collected, how it is collected, why it is collected, how it is used, who it is shared with, what are the benefits and risks, and what are the rights and choices of the data subjects. Transparency also means that the data collectors and processors are accountable for their actions and comply with the relevant laws, regulations, standards, and ethical guidelines.
Transparency is important for ensuring privacy when using big data analytics for several reasons. First, transparency respects the dignity and autonomy of the data subjects by acknowledging their interests and preferences regarding their personal data. Second, transparency fosters trust and confidence between the data subjects and the data collectors and processors by providing clear and accurate information and communication. Third, transparency enables informed consent and participation of the data subjects by giving them the opportunity to understand and agree to the data collection and use or to opt out or object if they wish. Fourth, transparency facilitates oversight and governance of the big data practices by allowing external audits, reviews, complaints, and remedies.
Some examples of how transparency can be implemented in big data analytics are:
Providing clear and concise privacy notices or policies that explain what data is being collected, how it is collected, why it is collected, how it is used, who it is shared with, what are the benefits and risks, and what are the rights and choices of the data subjects.
Obtaining explicit or implicit consent from the data subjects before collecting or using their data, or providing them with easy ways to opt out or object if they do not consent.
Implementing privacy by design and by default principles that ensure that privacy is considered and integrated throughout the entire lifecycle of big data analytics, from planning to implementation to evaluation.
Adopting privacy-enhancing technologies (PETs) that minimize or anonymize the personal data collected or used in big data analytics, or that enable secure encryption, pseudonymization, or aggregation of the data.
Establishing privacy governance frameworks that define the roles and responsibilities of the different actors involved in big data analytics, such as data owners, collectors, processors, analysts, users, regulators, auditors, etc., and that specify the rules and standards for privacy protection.
Conducting privacy impact assessments (PIAs) that identify and evaluate the potential privacy risks and benefits of big data analytics projects or initiatives, and that propose measures to mitigate or avoid the risks and enhance or maximize the benefits.
Providing mechanisms for feedback, consultation, participation, or co-creation of the data subjects in big data analytics projects or initiatives, such as surveys, focus groups, workshops, forums, etc.
Enabling access, correction, deletion, portability, or restriction of the personal data of the data subjects upon their request or demand.
Reporting on the outcomes and impacts of big data analytics projects or initiatives to the relevant stakeholders, such as the data subjects, regulators, customers, partners, society at large etc., in a transparent and accountable manner.
Maintenance of archived data (A), disclosure of how the data is analyzed (B), and continuity with business requirements (D) are also important considerations for ensuring privacy when using big data analytics. However they are not as important as transparency about the data being collected . Maintenance of archived data involves ensuring that the personal data stored in backup systems or historical records is protected from unauthorized access, modification or deletion. Disclosure of how the data is analyzed involves explaining the methods, techniques, tools, and algorithms used to process and interpret the personal data. Continuity with business requirements involves aligning the objectives, scope, and outcomes of big data analytics with the expectations, needs, and values of the organization and its stakeholders. These considerations are more related to the technical, procedural, and strategic aspects of ensuring that the personal data is processed in a secure, accurate, and relevant manner, which are necessary but not sufficient conditions for achieving the privacy protection of big data.
The Big Data World: Benefits, Threats and Ethical Challenges1
Big Data Privacy: A Technological Perspective And Review2
Big Data And Privacy What You Need To Know3

Collecting more data than necessary for recruitment violates the principle of data minimization, which requires limiting collection to what is adequate, relevant, and necessary. Transparency (A) deals with notice, localization (B) concerns storage jurisdiction, and quality (D) addresses accuracy.
"Data minimization: collect and process only the data that is necessary for the stated purpose."

One of the key principles of data protection is transparency, which means that individuals have the right to be informed about the collection and use of their personal data. This applies to video surveillance as well, especially in high security areas where the impact on privacy may be significant. Therefore, it is important to inform those affected by video surveillance about the purpose, scope, retention and access policies of the data collected.
Reference:
ISACA Certified Data Privacy Solutions Engineer (CDPSE) Exam Content Outline, Domain 2: Privacy Architecture, Task 2.1: Design privacy controls based on privacy principles and legal requirements, Subtask 2.1.1: Identify applicable privacy principles and legal requirements.
How can we comply with the data protection principles when using surveillance systems? | ICO

An audit log is a record of the activities and events that occur in an information system, such as an application hosting personal dat a. An audit log can help to monitor, detect, investigate and prevent unauthorized or malicious access, use, modification or deletion of personal data. An audit log can also help to demonstrate compliance with data protection laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). An audit log should capture the following information for each event: 9 The date and time of the event The identity of the user or system that performed the event The type and description of the event The outcome or result of the event The personal data that were accessed, used, modified or deleted The last user who accessed personal data is the most important information to capture in the audit log, as it can help to identify who is responsible for any data breach or misuse of personal data. It can also help to verify that only authorized and legitimate users have access to personal data, and that they follow the data use policy and the principle of least privilege. The last user who accessed personal data can also help to support data subjects' rights, such as the right to access, rectify, erase or restrict their personal data.
The other options are less important or irrelevant to capture in the audit log of an application hosting personal data. Server details of the hosting environment are not related to personal data, and they can be obtained from other sources, such as network logs or configuration files. Last logins of privileged users are important to capture in a separate audit log for user account management, but they do not indicate what personal data were accessed or used by those users. Application error events are important to capture in a separate audit log for system performance and reliability, but they do not indicate what personal data were affected by those errors.
Reference:
IS Audit Basics: Auditing Data Privacy, section 4: "Audit logs should be maintained for all systems that process PII." Data Protection Audit Manual, section 3.2: "Audit trails should be kept for all processing operations involving personal data." Audit Logging Best Practices, section 2: "An audit log entry should contain enough information to answer who did what and when."

The most important thing to consider when managing changes to the provision of services by a third party that processes personal data is the business impact due to the changes. Changes to the provision of services by a third party can affect the organization's ability to meet its business objectives and legal obligations related to data processing activities. For example, changes to the service level agreement (SLA), the scope of services, the security measures, the location of servers, etc., can have implications for the quality, availability, confidentiality, integrity, and compliance of personal data processing. Therefore, an IT privacy practitioner should assess and evaluate the business impact due to the changes, and ensure that they are aligned with the organization's privacy policies and applicable privacy regulations and standards. Reference: : CDPSE Review Manual (Digital Version), page 41