What is synthetic data?
As machine learning frameworks such as Tensorflow and PyTorch become easier to use and pre-designed models for computer vision and natural language processing become more common and powerful, a significant challenge data scientists face is data collection and processing.
Businesses often struggle to collect large amounts of data within a specific time frame to train accurate models. Manually labeling data is expensive and time-consuming to retrieve data. Synthetic data is an innovation that can help data scientists and businesses overcome these barriers and develop reliable machine-learning models faster.
Synthetic data sets are not constructed from records of actual events but are created by a computer program. The primary purpose of synthetic datasets is to provide a generic and robust way to train machine learning models.
Synthetic data useful for machine learning classifiers must have certain properties. Data can be categorical, binary, or numeric, but the dataset must be randomly generated. The random process to generate the data should be controllable and based on specific statistical distributions. You can also place random noise in your data set.
When using synthetic data in a classification algorithm, you should be able to customize the amount of class separation to make your classification problem easier or more complex, depending on the problem requirements. A regression task, on the other hand, can generate data using a non-linear generation process.
The privacy and security risks of synthetic data
Privacy concerns around user data collection and storage practices are growing. In the 2015 Cambridge Analytica data scandal, millions of users discovered that their data was being collected without their consent. In 2021, LinkedIn received public criticism after leaked user information was sold on the dark web.
The US Federal Privacy Act (1974) restricts the use of sensitive data directly related to an individual’s identity, known as personally identifiable information (PII). This includes social security numbers, phone numbers, and addresses. These laws restrict the collection and disclosure of PII and define specific sectors with strict regulatory requirements, including health (HIPAA) and finance (FCRA).
In recent years, governments worldwide have enacted stricter regulations to protect PII and personal data. Data privacy regulations, from GDPR in Europe to CRPA in California, require businesses to obtain explicit consent before collecting personal data, formalize users’ right to delete their data, and protect personal data from cybercrime. Following most of these regulations, images with biometric identifiers constitute personal data and are protected like any other form of personal data.
Any organization generating synthetic data must perform a privacy assessment to ensure that the synthetic data generated is not real personal data or too similar to such data. This privacy assurance assesses the extent to which synthetic data can identify a data subject and how much new data about that subject will be disclosed after successful identification.
Expected adverse effects of synthetic data on data protection:
- Output control can be complex—especially for complex data sets, the best way to ensure accurate and consistent output is to compare synthetic data with raw or human annotated data. However, this comparison requires access to the original data.
- The difficulty of plotting outliers—synthetic data can mimic accurate data. Therefore, some outliers in the original data may not be included in the synthetic data. However, outliers may be more critical than expected data points in some applications.
- Model quality depends on the data source—the quality of synthetic data is highly correlated with the quality of the original data and the model that generates it. Synthetic data can reflect biases in the original data. Also, manipulating the data set to create a fair synthetic data set can lead to inaccurate results.
How to mitigate synthetic data security risks
Data discovery tools
Data discovery tools help identify and locate sensitive data to ensure you effectively secure or remove this data. Organizations access and store massive amounts of data daily, making sensitive data discovery a major challenge even with proper data maintenance and storage. However, organizations that do not know where various types of data are located can encounter critical security issues and find it difficult to mitigate potential risks.
Sensitive data discovery is essential for organizations and industries required to comply with strict regulations. Financial services, healthcare institutions, government agencies, and telecommunication companies must adhere to strict sensitive data discovery requirements and follow industry-based regulations.
Here are notable benefits of sensitive data discovery tools:
- Effectively identify and locate sensitive data across known and previously unknown data sources.
- Optimize data storage and security.
- Classify sensitive data.
- Improve the organization’s understanding of the relevant risks and determine how to mitigate these risks.
AIOps
AIOps can help implement large-scale data monitoring and analysis to alert and contextually identify problems in an IT environment efficiently. It can also improve behavioral trend analysis and set up automated remediation for specific cases. Here are key features:
- Data classification and monitoring—AIOps analysis engines use known content types, patterns, and predefined policies to process data uploaded and created in the protected environment, classify and tag data, and monitor for access.
- Fraud detection—financial firms and insurers typically require enormous inputs, data types, and intensive processing to implement fraud detection. It combines text mining, social network analysis, anomaly detection, database searches, and predictive models. AIOps help make this process more efficient.
- Endpoint and network behavior modeling—modeling endpoint communications and behavior patterns can help detect subtle indicators of attack or compromise before any significant data breach or access can occur.
- Threat intelligence analysis—AIOps employs various internal IT operational data and threat intelligence from external providers to predict or circumvent attacks targeting cloud infrastructure, such as account hijacking.
Application Governance
Application governance includes the policies and rules businesses use to manage their applications. Application governance aims to increase data security, manage risk, and keep applications running smoothly.
Application governance helps development teams better plan and manage all aspects of an application, including how assets are deployed, how systems are integrated, and how data is protected.
Application governance programs ensure that complex application environments meet an organization’s security policies, best practices, and compliance requirements.
Data Privacy Management
Data privacy management enables organizations to protect sensitive data and remediate privacy breaches. Data privacy management tools assess the impact of technological changes on privacy, align IT activities with privacy regulations, and track events that may lead to unauthorized disclosure of personal data.
Data privacy management software helps organizations secure sensitive data in large, distributed environments and automate processes and policies to improve scalability and efficiency. It also reduces human error when complying with regulations and standards.
This type of solution usually provides the following features:
- Data classification—Automatically analyzes data patterns to identify personally identifiable information (PII) and determine sensitivity levels for security and compliance. This is also useful for reclassifying sensitive data when compliance requirements change.
- Data privacy regulation compliance—ensures personal data meets compliance requirements, provides auditing capabilities to compliance agencies, and identifies compliance risks.
- Remediation—provides remedial guidance on data privacy risks. This allows teams to prioritize and address privacy issues before they lead to security issues or compliance violations.
Conclusion
In this article, I explained the basics of synthetic data security and how to mitigate those risks:
- Data discovery tools—data discovery tools help identify and locate sensitive data to ensure you effectively secure or remove this data.
- AIOps—AIOps can help implement large-scale data monitoring and analysis to alert and contextually identify problems in an IT environment efficiently.
- Application governance—application governance includes the policies and rules that businesses use to manage their applications.
- Data Privacy Management—data privacy management enables organizations to protect sensitive data and remediate privacy breaches.
I hope this will be useful as you secure your synthetic data.
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About the Author
Hasnain Khalid is a passionate streaming and security enthusiast, who has proved his expertise on renowned platforms, including PrivacySavvy.com, ExtremeVPN.com, NetflixSavvy, and more. With a keen eye for online safety and a love for all strеaming matters, Hasnain combinеs his еxpеrtisе to navigatе thе digital world with confidеncе and providе valuablе insights to usеrs worldwidе.
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