StepAhead Local Differential Privacy

Smarter Anonymization. Better Data.
StepAhead's on-premise differential privacy solution produces anonymized data of higher accuracy, helping AI models deliver superior performance while unlocking value from previously inaccessible sources.
The AI Privacy Challenge
Explosive AI model demand for training data is meeting growing privacy barriers. The rapid expansion of vertical AI applications and growth of edge devices has fueled unprecedented demand for high-quality model training data. But escalating privacy concerns and regulations (e.g., HIPAA, GDPR, CCPA) mandate that sensitive data be anonymized prior to downstream use.
While Differential Privacy (DP) has become the "gold standard" for data anonymization, existing solutions have critical limitations:
Require sharing sensitive data in centralized collaboration environments
Global DP solutions are often suitable when a central curator is fully trusted and sharing of sensitive data is acceptable, but business rules or privacy regulations prevent many organizations from sending data outside their security perimeter.
Apply uniform privacy settings that can degrade accuracy
Most applications apply a single accuracy/privacy tradeoff value (or epsilon) to an entire file, which is typically based on the most sensitive attributes in the data set. Without an option to fine-tune ε precisely for different fields, overall accuracy is reduced. 
Support only numeric data types
Commercial DP solutions tend to function best with numeric data, but offer limited support for non-numeric data, such as alphanumeric strings, dates, phone numbers, addresses, emails, categorical fields, names, etc.
Lack intuitive interfaces
DP solutions can be challenging to use or deploy. They may require coding, significant technical teams or resources, and/or deep data science expertise
Guaranteed Privacy, Actionable Data.
Differential Privacy (DP) is a privacy-enhancing technology that protects sensitive data by adding mathematically calibrated "noise," safeguarding sensitive information and ensuring anonymity, while preserving data utility for analysis. DP applies a specific privacy/accuracy tradeoff or noise level to data, referred to as epsilon (ε) or a "privacy budget."
Gold Standard Privacy
DP has emerged as the "gold standard" for anonymization when valuable data containing sensitive information, such as    PII or PHI, is used for analysis. 
Adds Controlled Noise
DP introduces a small amount of random "noise" to source data or to query results, which obscures sensitive info, while maintaining data accuracy.
Unlocks Safe Insights
It fosters trust by allowing for statistical analysis and AI/ML model training without compromising personal information.
Differential Privacy helps to not only reduce risk, but unlock data for AI and analytics use cases that was previously difficult to do.
Gartner Hype Cycle for Privacy, 2025
The StepAhead Solution
On-premise anonymization with Local Differential Privacy (LDP) by StepAhead generates safe and compliant data for downstream product development, analysis or AI/ML model training.
StepAhead's Tarmiz delivers superior data anonymization through local differential privacy, addressing the critical limitations of existing DP solutions. Tarmiz also allows users to redact or pseudonymize sensitive data. 
Unmatched Ease of Use
Simple, on-premise anonymization with an intuitive UI. No complex data clean rooms, third-party 
trusted curators or data chain-of-custody monitoring required.
Granular, Attribute-level Privacy Control
Smarter anonymization means precision fine-tuning of the privacy setting (ε), maximizing 
utility for less sensitive attributes while ensuring robust protection for more sensitive fields. 
No more "one size fits all."
Robust Enterprise Scalability
Supports all major structured data types - not just numeric, and it processes tens of millions 
of records in near real-time. 
Flexible, Integrated Deployment
Choose automated or on-demand processing for seamless integration into existing data workflows.
Current differential privacy solutions apply a uniform protection [ε] to all data features, including less sensitive ones, which degrades performance of downstream tasks.
Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
Proceedings of Machine Learning Research, Issue 258, 2025
A single, universal privacy/accuracy setting 
with conventional DP offerings
Precision, attribute-level privacy/accuracy 
control with StepAhead's Tarmiz
The StepAhead Advantage
Our attribute-level epsilon control, on-premise anonymization, and broad data attribute support deliver tangible business benefits that conventional differential privacy solutions can't match. 
Accelerate Adoption
Lower technical barriers enable effective privacy protection on-premise, without specialized data science teams. Install & run, on-demand or automated, to create new data sets safe to license, share or use.
Higher Data Accuracy Boosts ROI
Achieve 10-25% higher data accuracy post-anonymization for the same privacy budget, leading to better AI model performance & reduced training time & expense.
Unlock New Data Sources
Glean valuable insights from previously siloed or inaccessible data where traditional sharing or centralized anonymization methods are prohibited.
Sectors & Use Cases Supported
StepAhead's local differential privacy solution enables safe data usage across industries and use cases where privacy and accuracy are paramount, and where sensitive data must remain within the security perimeter.
Product Development
Use safe, production-like data for building and testing applications without exposing sensitive information
Healthcare
Analyze patient data without violating HIPAA regulations, enable medical research with protected health information
Financial Services
Detect fraud in transaction data without revealing personal information, comply with financial privacy regulations
IoT & Telemetry
Collect device performance and usage data while protecting user privacy, enable edge device analytics
Federated Learning
Perfect for decentralized ML training where data cannot be centralized or shared directly
Government
Analyze real-time sensor data from individual utility components, while protecting identities from the central monitoring agency.
The rapid growth of smart devices—phones, wearables, IoT sensors, and connected vehicles—has led to an explosion of continuous time series data that offers valuable insights in healthcare, transportation, and more. However, this surge raises significant privacy concerns, as sensitive patterns can reveal personal details. While traditional differential privacy (DP) relies on trusted servers, local differential privacy (LDP) enables users to perturb their own data.
Cooperative Local Differential Privacy: Securing Time Series Data in Distributed Environments
IEEE International Conference on Intelligent Mobile Computing, 2025
About StepAhead
Founded in 2024 and headquartered in Boston, MA, StepAhead develops superior data anonymization technology using local differential privacy. Our flagship product, Tarmiz, allows enterprises to protect sensitive information in valuable data sets while preserving high utility - making them safe to license, share or use for AI model training, analysis, and product development.
Leadership Team
StepAhead is led by experienced technology and business leaders with deep expertise in data management, privacy enhancing technologies, and enterprise software.
Todd Lowdon 
CEO
Rahman Abuhasan 
Co-Founder & Chief Development Officer
Bill Olsen 
Co-Founder & CTO

Contact us at info@stepahead.dev or 617.209.9294. 

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