CDPSE Exam Dumps - Certified Data Privacy Solutions Engineer

A peer-to-peer (P2P) system architecture is a network model where each node (peer) can act as both a client and a server, and communicate directly with other peers without relying on a centralized authority or intermediary. A P2P system architecture best supports anonymity for data transmission, by providing the following advantages:

It can hide the identity and location of the peers, by using encryption, pseudonyms, proxies, or onion routing techniques, such as Tor1 or I2P2. These techniques can prevent eavesdropping, tracking, or censorship by third parties, such as Internet service providers, governments, or hackers.

It can distribute the data across multiple peers, by using hashing, replication, or fragmentation techniques, such as BitTorrent3 or IPFS4. These techniques can reduce the risk of data loss, corruption, or tampering by malicious peers, and increase the availability and resilience of the data.

It can enable the peers to control their own data, by using consensus, validation, or incentive mechanisms, such as blockchain5 or smart contracts. These mechanisms can ensure the integrity and authenticity of the data transactions, and enforce the privacy policies and preferences of the data owners.

Before an organization can respond to data subject access requests (DSARs), it needs to have a clear understanding of the data in its possession, such as what types of personal data are collected, where they are stored, how they are processed, who has access to them, and how long they are retained. This will help the organization to locate and retrieve the relevant data for each DSAR, and to ensure that the data are accurate, complete and up to date. Understanding the data in its possession will also help the organization to comply with other data protection principles and obligations, such as data minimization, purpose limitation, security and accountability.

The other options are less important or irrelevant to do first. Investing in a platform to automate data review may help to speed up the response process, but it does not guarantee that the organization has identified all the data sources and categories that are subject to DSARs. Confirming what is required for disclosure is also important, but it depends on the specific request and the applicable law or regulation. Creating a policy for handling access requests is a good practice, but it should be based on a thorough understanding of the data in its possession.

Synthetic data is generated artificially to mimic patterns without containing real personal data, making it the strongest method to embed privacy by design in AI training. De-identification (A) reduces but does not eliminate reidentification risk; consent (B) addresses lawfulness, not privacy-preserving training; privacy notices (D) provide transparency but not technical risk reduction.

“Synthetic data allows AI model training without exposing real personal information.”

Cloud vendor agreements are the best way to ensure an organization’s data retention requirements will be met in the public cloud environment because they define the roles, responsibilities and obligations of both parties regarding the collection, storage, processing and disposal of data in the cloud. They also specify the terms and conditions for data protection, security, privacy, compliance and auditability12. Data classification schemes, automated data deletion schedules and service level agreements (SLAs) are useful tools to manage and monitor data retention, but they do not guarantee that the cloud vendor will adhere to the organization’s data retention requirements or that they will be enforceable in case of disputes.

The greatest benefit of adopting data minimization practices is that the associated threat surface is reduced. Data minimization is a privacy principle that states that personal data should be adequate, relevant, and limited to what is necessary for the purposes for which they are processed. Data minimization helps to protect data privacy by reducing the amount and type of personal data that are collected, stored, processed, or shared by an organization. This in turn reduces the exposure of personal data to potential threats, such as unauthorized access, use, disclosure, modification, or loss. References: : CDPSE Review Manual (Digital Version), page 29

The best way to ensure an organization’s enterprise risk management (ERM) framework can protect the organization from privacy harms is to complete a privacy risk assessment. A privacy risk assessment is a systematic process of identifying, analyzing, evaluating, and treating the privacy risks that may affect the organization’s objectives, operations, stakeholders, and reputation. A privacy risk assessment helps to align the ERM framework with the privacy requirements, expectations, and obligations of the organization, as well as to prioritize and mitigate the privacy risks that may cause privacy harms. Privacy harms are the adverse consequences or impacts that may result from the unauthorized or inappropriate use, disclosure, or loss of personal data, such as financial loss, identity theft, discrimination, reputational damage, emotional distress, or physical harm.

According to the General Data Protection Regulation (GDPR), data subjects have the right to withdraw their consent for processing their personal data at any time. However, this right does not apply when the processing is necessary for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes, in accordance with Article 89(1) of the GDPR1.

Any organization collecting information about EU residents is required to operate with transparency in collecting and using their personal information. Chapter III of the GDPR defines eight data subject rights that have become foundational for other privacy regulations around the world:

Right to access personal data. Data subjects can access the data collected on them.

One of the privacy requirements related to the rights of data subjects is the right to access, which means that individuals have the right to obtain a copy of their personal data, as well as information about how their data is processed, by whom, for what purposes, and for how long. To meet this requirement, an organization’s technology stack should incorporate features that allow individuals to have direct access to their data, such as self-service portals, dashboards, or applications. This way, individuals can exercise their right to access without relying on intermediaries or manual processes, which can be inefficient, error-prone, or insecure. References: : CDPSE Review Manual (Digital Version), page 137