Traditional Data Collection

Defining Sensitive Data

Understanding what constitutes sensitive data is fundamental for organizations aiming to comply with regulatory frameworks and ensure robust data protection. Sensitive data, as defined by various regulatory bodies, involves types of information that, if compromised, could lead to significant harm or discrimination against individuals. This chapter will delve into the specifics of sensitive data categories guided by the General Data Protection Regulation (GDPR) and other relevant frameworks.

Traditional Data Collection Models

The methodologies for collecting sensitive data have evolved considerably over the years. Traditional data collection models serve as the foundation for understanding current practices and the inherent vulnerabilities that necessitate robust data protection measures. This chapter outlines some of the key traditional methods organizations have employed to gather and manage sensitive data.

Data Lifecycle

Understanding the data lifecycle is crucial for organizations to manage sensitive data responsibly, ensuring privacy and compliance with regulatory frameworks. The data lifecycle encompasses various stages, each with its own set of processes and challenges. This chapter delves into the typical stages of the data lifecycle and highlights a critical issue related to data collection and context retention.

  1. Collection: Data is gathered from various sources, including user input, public records, and third-party providers.
  2. Storage: Data is stored in databases, data warehouses, or other storage systems.
  3. Processing: Data is analyzed, transformed, and used for various purposes, such as marketing, personalization, and decision-making.
  4. Sharing: Data may be shared with third parties, such as business partners or service providers.
  5. Disposal: Data is eventually deleted or anonymized when no longer needed.

A critical issue arises at the very beginning of this lifecycle. While data is often collected through specific interactions (e.g., filling out a form), the link to this original source is frequently lost. This means that data becomes detached from its context, making it difficult to trace its origin and for individuals to exercise control over its usage.

Data Ownership and Control

In the traditional model, organizations typically assert ownership over the data they collect. This grants them significant control over data usage, sharing, and retention. Individuals have limited rights to access, modify, or delete their data.

Privacy Concerns

Traditional data collection practices raise substantial privacy concerns:

The GDPR, among other regulations, has introduced stricter data protection measures. It grants individuals specific rights, including the right to access, rectify, erase, and restrict the processing of their personal data. However, enforcing these rights can be challenging under traditional data collection models due to the lack of data traceability and control.

To address these challenges, we propose a new approach based on the concept of Data Identities (DIDs).

A DID represents a unique identifier linked to a specific data collection event. It acts as a container for associated data and provides individuals with granular control over their information. By establishing a clear link between data and its origin, DIDs enable greater transparency, accountability, and user empowerment.


Revision #1
Created 4 August 2024 11:35:01 by Thorsten Zoerner
Updated 4 August 2024 11:46:21 by Thorsten Zoerner