Most research on privacy assumes that the data sets being protected have value to hackers, who will work hard to get their hands on them. In practice, this is frequently true. But it’s not always the case. We need to be smart about the ways we decide what security measures make sense for a given dataset and implement them accordingly. Synthetic datasets can be created with more intentionality about their security needs, allowing us to reduce security costs incurred in many use cases.
The Security Burden on Privacy is a critical aspect of GDPR that will have a huge impact on how companies collect and use customer data. Learn more about this topic in this article.
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The Need For Data
Data is an important factor in the decision-making process. It aids in the development of a deeper knowledge of your target clients, allowing you to establish efficient marketing tactics and boost sales.
However, if this data isn’t accurate, dependable, or trustworthy, it won’t be of any use to your business.
Data synthesis has been used for decades, but its popularity is rising as more businesses see its potential as a tool for reducing risk while accomplishing goals.
Synthetic data also provides many other benefits:
- It enables businesses to test new products or services without having access to real data sources
- It allows companies with limited resources (such as startups) to conduct large-scale experiments at low costs
- It helps companies avoid potentially costly mistakes when creating new products
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Ensuring Compliance And Privacy With Synthetic Data
Synthetic data can be produced using a range of algorithms and methods. For example, it can be created from a variety of sources, such as:
- Statistical data (e.g., demographics)
- De-identified personal information (e.g., names or Social Security Numbers)
- Publicly available personal information from public registries or databases (e.g., voter registration records)
Synthetic data can also be used as an alternative to real data in a range of circumstances:
- When the use of real data has privacy implications but the use of synthetic data would not have those same privacy implications; for example, many medical research studies have ethical approval to use de-identified patient records, but these same studies often need access to additional information about individuals that could not legally be obtained without their informed consent unless there were some other legal justification for doing so—such as obtaining this additional information from another source which does not require informed consent because it is publicly available or de-identified itself; this may include public registries or databases such as voter registration records where citizens are required by law to register with the government before being allowed vote in elections; hence researchers could potentially use these existing datasets instead
Securing Synthetic Data Through Encryption And Access Controls
Securing synthetic data is a lot like securing any other type of sensitive information:
- Use access and authorization controls. You can create policies that restrict who has access to what types of data, and you can use encryption or tokenization when transmitting the information over a network.
- Encrypt the data in storage and at rest. Synthetic data may be stored in databases or files on disk, but it should still be encrypted before being written and read from storage media (like hard drives). The same principle applies if you’re storing synthetic data in any other kind of storage system—you must encrypt it before writing it to disk or sending it over the wire!
Adopting A Risk-Based Approach To Monitoring And Auditing
A risk-based approach to monitoring and auditing helps you develop an understanding of your data and how it’s being used. A good place to start is by identifying the risks associated with your AI model, its training data, pipeline, and environment. Examples of security concerns include:
- Misuse or non-compliance with privacy laws
- Using synthetic data without a proper license or authority (e.g., using synthetic personal health information when you don’t have proper authorization).
- The possibility of releasing sensitive information when sharing AI models with third parties.
Once you understand these risks, you can begin developing policies and procedures that help protect against them. Some possible measures include:
- monitoring access controls over intellectual property;
- auditing applications before they become part of a production pipeline;
- making sure no sensitive information is released into the public domain;
- ensuring employees sign NDAs before accessing proprietary material;
- requiring secure storage for all electronic documents containing sensitive financial/personally identifiable information (PII);
- enforcing password strength requirements for all employees who handle sensitive data every day;
- having regular reviews conducted by an outside auditor to ensure compliance with internal policies related to employee job roles/responsibilities
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Conclusion to Will Synthetic Data Reduce The Security Burden On Privacy
The benefits of synthetic data are many. It can be used to help organizations meet compliance, privacy, and security standards. It can help organizations protect the privacy of their customers and employees by reducing the risk that personal information is compromised or used inappropriately. Synthetic data also helps detect risks that might otherwise go unnoticed because they’re masked by in-comprehensive data sets or complex correlations between multiple accounts or because they occur when transactions processed in real-time are viewed out of context. Contact us for more insight into Will Synthetic Data Reduce The Security Burden On Privacy.
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