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Description

Privacy engineering is a growing discipline that overlaps with various fields, including data engineering, information security, software development, and privacy law. The primary objective of this field is to ensure that personal data is managed and handled in a manner that complies with legal standards while also safeguarding the privacy of individuals to the greatest extent possible. While security engineering serves as both a foundational element of privacy engineering and a standalone area of expertise, its main focus is on ensuring the secure management and storage of sensitive data broadly. Organizations that handle sensitive or personal data, or both, must prioritize privacy and security engineering practices. This necessity becomes even more critical for those engaged in their own data engineering or data science activities, as the complexities of data management grow. Ultimately, integrating these principles is vital for building trust and maintaining compliance in today's data-driven landscape.

Description

Developed and continuously improved by a dedicated team of professionals specializing in differential privacy, this system is actively utilized by organizations such as the U.S. Census Bureau. It operates on the Spark framework, seamlessly handling input tables with billions of entries. The platform offers an extensive and expanding array of aggregation functions, data transformation operations, and privacy frameworks. Users can execute public and private joins, apply filters, or utilize custom functions on their datasets. It enables the computation of counts, sums, quantiles, and more under various privacy models, ensuring that differential privacy is accessible through straightforward tutorials and comprehensive documentation. Tumult Analytics is constructed on our advanced privacy architecture, Tumult Core, which regulates access to confidential data, ensuring that every program and application inherently includes a proof of privacy. The system is designed by integrating small, easily scrutinized components, ensuring a high level of safety through proven stability tracking and floating-point operations. Furthermore, it employs a flexible framework grounded in peer-reviewed academic research, guaranteeing that users can trust the integrity and security of their data handling processes. This commitment to transparency and security sets a new standard in the field of data privacy.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Java No 
Python No 
SPARK No 

Integrations

Java Yes 
Python Yes 
SPARK Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) Yes 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Kodex

Website

heykodex.com

Vendor Details

Company Name

Tumult Analytics

Founded

2019

Country

United States

Website

www.tmlt.dev/

Product Features

Data Privacy Management

Access Control No 
CCPA Compliance No 
Consent Management No 
Data Mapping No 
GDPR Compliance No 
Incident Management No 
PIA / DPIA No 
Policy Management No 
Risk Management No 
Sensitive Data Identification No 

Product Features

Data Privacy Management

Access Control No 
CCPA Compliance No 
Consent Management No 
Data Mapping No 
GDPR Compliance No 
Incident Management No 
PIA / DPIA No 
Policy Management No 
Risk Management No 
Sensitive Data Identification No 

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