What are the key principles of data privacy?

Questions arise: How long will my data be available to third parties, and for what purposes? European GDPR and California CCPA are legal frameworks governing data privacy. They safeguard personally identifiable information (PII), permitting sharing with third parties only after customer consent, including the Right to Access, Right to be Forgotten, and Right to Erasure. During the installation of any application, a "Privacy Policy" pop-up appears in the final phase, detailing the data collected and its intended purpose. Users typically select "I agree." Additionally, the AWS S3 policy functions as a safety valve, specifying the retention period for historical files.

The most crucial principle is accountability. When you're entrusted with someone's Personal Identifiable Information (PII), it's your duty to safeguard it and use it only for the purposes you've informed your customers about. This data must remain confidential, meaning it should be securely protected. To understand the importance of confidentiality, picture your credit card or bank account details becoming public knowledge. Now, expand that to imagine it's leaked to thousands of people. The consequences are severe and can include businesses closing down, damage to an organization's reputation, and legal actions being taken.

Principle: Personal data should only be collected for specified, explicit, and legitimate purposes. Example: In 2018, Facebook faced criticism for the Cambridge Analytica scandal. The company had allowed the collection of personal data for academic purposes but failed to ensure it was used only for that purpose. The incident highlighted the importance of clearly defined and limited data usage.

Data privacy, a linchpin in the field of data engineering, goes beyond legal compliance. It involves safeguarding personal information from unauthorized access, use, or disclosure. Purpose Limitation: Precision is key. Clearly define the purposes for data collection and processing. Align data engineering initiatives with these defined objectives, avoiding unnecessary handling of sensitive information. This principle ensures data is utilized for its intended purpose.

As far as Data Privacy is concerned in related to Data Engineering , it should be well outlined as part data discovery and should be discussed in detail before the actual implementation starts , in the current world scenario data privacy is a key point to consider at the time of development so that the data engineering pipelines we develop are robust from Day 1 with all the possible security controls in place. Also Data Engineers should also develop pipeline which are future proof to accommodate any future regulations that may come up in similar lines. The attributes we consume has to clearly analyzed and tagged into PHI,PCI and PII attributes and should be utilized with consent only we get real business value out of it.

Data privacy is a must to keep our customers secure however Data privacy can sometimes get misinterpreted, one shouldn't over complicate the data privacy by over engineering the data privacy need otherwise it will soon become hard to maintain.

The precision of labels that privacy tools will use to work always depends about the precision of data. So you need to check your plan to manage and assign labels to data based several defined level of data precision. You always want that your data can be manage well.

A credit reporting agency mistakenly includes inaccurate information in an individual's credit report. As a result, the person is denied a loan. This highlights the need for organizations to regularly update and verify the accuracy of the data they hold.

Yes, accuracy of data is important or otherwise wrong decision making will be made and thereby judgements will not be correct in any applications

Data Quality is a key topic for this principle. If Data Quality is not ensured, ML models fed by data won't be accurate or even wrong (Garbage In Garbage Out). Data Quality should be seen as a pre-requisite for any data-related task. For this purpose, DQ Control Models are a perfect fit.

According to the data model, architecture and functionality, data pipelines can apply some peculiar security and privacy measures depending on use case: - To ingest data, sensitive data can be separated and anonymized from any other data because a private data vault model is implemented. - To share data with third parties, you want to apply multi-factor authentication processes to use the service and apply methods to unlink sensitive data with users. They all will share or use encryption, monitoring, logging, auditing or recovery processes; but depending on the purpose they may use specific privacy and security processes.

Fortify your data. Integrate robust security throughout the data lifecycle in data engineering processes. Encrypt sensitive information, enforce strict access controls, and conduct regular security audits to ensure data integrity. Picture a secure vault where each staff member has a unique key or passcode granting them access only to the areas pertinent to their roles; Strict access controls in data engineering emulate this scenario, limiting entry to authorized personnel and preventing unwarranted exposure of sensitive data of any kind.

The security principles mentioned here all are valid points. No more explanations are necessary. Unless data is protected by means of security, we will be in danger zone even in day to day life

The 2017 Equifax data breach exposed sensitive personal information of millions. The breach occurred due to a failure to patch a known vulnerability. This emphasizes the importance of robust cybersecurity measures to safeguard personal data.

Ensuring security principle of data privacy begins by anonymizing or pseudonymizing sensitive information, reducing the risk of personal data exposure. Encryption is employed during data transmission and storage, safeguarding against unauthorized access Access controls limit who can view or manipulate data. Regular audits and monitoring help identify vulnerabilities or breaches, allowing for swift remediation. Data masking and tokenization techniques can be applied to further protect data at rest. Ultimately, data engineers must adhere to privacy regulations like GDPR and HIPAA, maintaining a keen awareness of evolving compliance requirements and adjusting data processes accordingly to safeguard individual privacy.

In my experience its best to assign accountability as close to the business as possible. In the analytics world this is more commonly referred to as the "Data Owner". Responsibility should sit with the Data Engineers and technical teams but they are responsible not accountable.

Individuals must be given the option to opt in or out before collecting or processing their personal data. Informed consent should be obtained before this process, with individuals made aware of how their data will be utilized. From the very beginning, make sure to incorporate privacy concerns into the creation and establishment of systems and methods. It's important that privacy is given priority in default settings so that users are not forced to go to great lengths to safeguard their privacy. To safeguard against unwanted intrusion, alteration, or destruction of data, it is critical to establish foolproof security protocols. These should include measures such as encryption, frequent audits of security systems, as well as access controls.

A robust data governance framework is the key pillar to ensure accountability. By establishing data lineage and auditing mechanisms, data engineers can track the lifecycle of data, providing transparency and traceability in compliance efforts Collaborating with data protection officers and compliance teams, data engineers can develop and maintain data cataloging systems that document data usage and purpose, aiding in accountability assessments. Regularly reviewing data access policies, conducting privacy impact assessments, and incorporating privacy-by-design principles into data pipelines, they can proactively protect data privacy and enable organizations to meet their accountability obligations in an increasingly data-driven world.

he European Union's General Data Protection Regulation (GDPR) requires organizations to appoint a Data Protection Officer (DPO) responsible for ensuring compliance. This promotes a proactive approach to data protection and accountability.

The user consent give to business authorization to process data based in specific topics display by business to user to give new features. You need to build a consent management system would be integrate with data pipelines management to support this common cases. The user is accountable about this data processing in these cases, if business did well in consent implementation by both legal and technical views.

One element that can help in transparency is organising data by purpose. This allows you to also split privacy sensitive data from other data, allowing easier data privacy facilitation such as access and removal. This helps both the users whose data is stored, and analysts who analyse the data.

For example, let’s consider a scenario I faced where a company collects personal data from its customers for marketing purposes. The transparency principle requires the company to inform its customers about the purpose of data collection, the types of data collected, the methods used to collect data, and the risks associated with data processing. The company must also obtain the customers’ consent or legal basis for processing their data, and provide them with the means to access, rectify, erase, restrict, or port their data, or to object or withdraw their consent.

Be transparent about data usage. Communicate policies and practices. In data engineering, implement straightforward consent processes, ensuring stakeholders are well-informed and can make meaningful decisions about their data. Consider a Health Tech company that collects and processes health-related data for personalized wellness applications. Transparency is crucial to establishing trust with users who share sensitive health information.

When a user installs a new app, they are prompted to review and accept the app's privacy policy. This transparency ensures that individuals are aware of how their data will be used before providing consent.

?Alguna vez te has preguntado por qué un ingeniero de datos debe estar al tanto de las implicaciones legales cuando realiza la gestión de datos? Una de las razones principales radica en la existencia del Reglamento General de Protección de Datos en la Unión Europea, conocido por sus siglas en inglés como GDPR. Según el Artículo 12 debes proporcionar información específica a la gente de manera concisa, clara y sencilla. Esto incluye todas las actividades del procesamiento de datos. Es decir se debe aplicar el principio de transparencia que “requiere que los ingenieros de datos proporcionen a los interesados información clara y concisa sobre el propósito, el alcance, los métodos y los riesgos del de procesamiento de datos”.

An example could be a bank that uses machine learning algorithms to determine creditworthiness. The bank must ensure that the algorithms do not discriminate against certain groups of people based on their race, gender, or other protected characteristics. The bank must also ensure that the algorithms are transparent and explainable, so that customers can understand how their creditworthiness is being evaluated and challenge any decisions that they believe are unfair or biased.

The fairness principle in data privacy is crucial because it ensures that data processing respects the rights and dignity of individuals, preventing harm or discrimination that could arise from biased or unethical data practices. Its application fosters trust and accountability in data handling, requiring data engineers to engage with and consider the perspectives of those affected by their decisions, thereby promoting more ethical and socially responsible use of data.

An employer using surveillance technology to monitor employees without their knowledge may violate the fairness principle. Balancing the need for monitoring with the right to privacy is crucial to ensure fairness in data processing.

Quest?o delicada a equidade. N?o pelo conceito embarcado no princípio, mas pelas conhecidas formas de enviesamento discriminatório. O enviesamento discriminatório muitas vezes ocorre por baixa diversidade em times de desenvolvimento, falta de transparência na administra??o de algoritmos e dados pessoais e outros fatores. O tema é abordado com maior profundidade no livro "Algoritmos de Destrui??o em Massa", de Cathy O'Neil.

I believe that data fairness is a term more aligned with machine learning rather than data engineering. In machine learning model fairness ensures no discrimination is being done on the basis of a person's gender, socio-economic class, race and ethnicity by the model.

3. Yahoo Data Breaches (2013-2016): Yahoo suffered two major data breaches that affected over a billion user accounts. The breaches, which were disclosed several years after they occurred, included stolen personal information such as names, email addresses, and hashed passwords. This incident emphasized the importance of timely and transparent communication about data breaches. 4. NSA Surveillance Leaks (2013) These stories emphasize the real-world impact of failing to uphold principles of data privacy, whether through inadequate security measures, unauthorized data harvesting, or government surveillance. They also highlight the need for ongoing efforts to protect individuals' privacy in an increasingly interconnected digital landscape.

Data Annotation is an important task to remove/replace the personal or confidential information of a user before data is being used to ensure privacy.

Data engineers play a vital role in maintaining data privacy compliance by establishing and maintaining secure data infrastructure They implement robust encryption methods to protect data at rest and transit, ensuring that sensitive information remains confidential Data masking & anonymization techniques are employed to prevent the exposure of personal data, and complying with privacy regulations Access controls are carefully defined to restrict data access to authorized personnel only, reducing the risk of unauthorized data breaches Continuous monitoring & auditing of data pipelines enable data engineers to promptly identify, rectify any compliance issues.

There are some factors which are crucial for ensuring robust data privacy. For high-risk processing activities, we can perform Data Protection Impact Assessments. Understanding and complying with the regulations governing the international transfer of personal data, for example the GDPR's requirements for data transfer outside the European Union. Establishing data governance policy can help in defining roles and responsibilities, ensuring data quality and establishing procedures for data access and sharing. Establishing Data Retention Policies, cross functional collaboration, Regular Updates and Compliance Monitoring, can help maintain a strong and effective privacy framework.

Obtain permission from people before collecting their personal data and ensure you collect only the data that is strictly necessary for the purpose at hand