Wiesloch, Germany

Martin Haerterich

This inventor holds 27 USPTO granted patents and 11 published patent applications and 4 EPO patents, primarily in Data Security. Top assignees: Sap Se, Sap Ag. Active years: 2014-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Martin Haerterich. Retrieved from https://idiyas.com/inventor/martin-haerterich

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 27 

% Patents Active = 96.3

 

Average Co-Inventor Count = 3.6

ph-index = 7

Forward Citations = 131(Granted Patents)


Company Filing History:


Years Active: 2014-2026

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Areas of Expertise:
Vulnerability Detection
Data Obfuscation
Privacy Enhancement
Differential Privacy
Anomaly Detection
Encrypted Databases
Honeypots
User Classification
Access Control
Generative Models
Machine Learning
Test Automation
27 patents (USPTO):Explore Patents

Title: Martin Haerterich: A Pioneer in Data Privacy Innovations

Introduction

Martin Haerterich is a distinguished inventor and innovator based in Wiesloch, Germany. With a prolific portfolio of 23 patents, he has significantly contributed to the realms of data privacy and deep learning technologies. His work not only enhances the security of personal information but also paves the way for more robust and interpretable machine learning models.

Latest Patents

Among Haerterich's most recent innovations are two noteworthy patents:

1. **Data Obscuring for Privacy-Enhancement**: This patent outlines systems and methods designed to obscure private information within input data. The system utilizes an encoder model to create a latent space representation of the data unit. It introduces multi-dimensional noise to this representation, ensuring that the original information remains confidential. A decoder model is then applied to generate an obscured data unit, protecting sensitive information from unauthorized access.

2. **Interpretability Framework for Differentially Private Deep Learning**: This invention addresses the challenges associated with re-identifying data points in a dataset. By calculating privacy parameters based on a bound for an adversarial posterior belief, the framework governs the application of a differential privacy algorithm. This ensures that the function evaluated over the dataset maintains the highest privacy standards while being interpretable and usable.

Career Highlights

Martin Haerterich has an impressive career trajectory, having worked at prominent companies including SAP SE and SAP AG. His experiences at these institutions have been pivotal in shaping his understanding of the intersection of technology, data privacy, and user security.

Collaborations

Throughout his career, Haerterich has collaborated with notable professionals in the field. He has worked alongside experts such as Florian Kerschbaum and Axel Schroepfer, contributing to innovative projects that address critical challenges in data protection and machine learning.

Conclusion

Martin Haerterich’s contributions to innovations in data privacy highlight his role as a significant figure in the field. With his wealth of patents and a focus on enhancing the interpretability and security of data, he continues to influence the advancement of technology in meaningful ways. His work serves as a foundation upon which future innovations will be built, ensuring a safer digital landscape for users worldwide.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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