Data privacy concerns and the increasing demand for secure, high-quality datasets have pushed companies to rethink traditional anonymization methods. MOSTLY AI, a Vienna-based synthetic data company, offers a solution by generating privacy-preserving synthetic datasets that retain the statistical properties of original data. This emerging approach addresses the limitations of conventional techniques like data masking and obfuscation, which have proven ineffective in the era of big data. By leveraging advanced AI, MOSTLY AI’s platform enables organizations to utilize sensitive data without compromising individual privacy.
Why are current anonymization methods inadequate?
Outdated anonymization practices fail to provide sufficient privacy in the context of big data. According to Alexandra Ebert, Chief AI and Data Democratization Officer at MOSTLY AI, these methods stem from a time when organizations handled only limited data points per individual. She explains that with hundreds or thousands of data points now collected per customer, traditional techniques often allow re-identification of individuals even after anonymization. This affects data quality and reduces its value for AI and machine learning applications.
How does synthetic data address privacy and data utility?
MOSTLY AI’s synthetic data platform uses generative AI to mimic the behavior and patterns of real data while ensuring complete anonymity. By excluding uniquely identifiable individuals and retaining only generalized patterns, the technology safeguards privacy while preserving data integrity. Ebert emphasizes that synthetic data eliminates the trade-off between privacy and usability, enabling businesses to generate datasets that are both compliant and valuable. These datasets are created in a separate generative process, ensuring that no original data is altered or shuffled.
A comparison with earlier efforts highlights the novelty of synthetic data. Traditional anonymization required specialized expertise and often resulted in incomplete datasets. Conversely, MOSTLY AI’s tools simplify the process, automating privacy protection and making synthetic data generation accessible to organizations of varied sizes. The company also launched an open-source synthetic data SDK, enabling developers to integrate privacy-safe solutions into their workflows more easily.
MOSTLY AI collaborates with major enterprises, including CitiBank and the US Department of Homeland Security, helping them optimize AI-driven solutions. The company has raised $31 million and focuses on aligning its technology with regulatory standards like GDPR. The open-source initiative further expands its mission by encouraging data democratization across sectors, from enterprises to NGOs.
Ebert stresses that synthetic data is particularly valuable for startups that lack access to proprietary datasets. By facilitating secure data sharing between enterprises and startups, synthetic data accelerates innovation and fosters collaboration. For example, a financial institution can provide anonymized transaction data to startups developing AI-driven financial products, benefiting both parties.
Synthetic data emerges as an essential tool for balancing data privacy with the demand for actionable insights in machine learning and AI. By addressing the inadequacies of traditional anonymization, this technology bridges the gap between privacy compliance and the need for robust datasets. As data democratization gains traction, synthetic data could play a significant role in driving responsible AI innovation and cross-sector collaboration.
