Today, data privacy and information security are two fundamental pillars for any organization handling large volumes of sensitive data. This is especially true when training Machine Learning (ML) and Natural Language Processing (NLP) models, which often require analyzing vast amounts of information. At Kriptos, we understand that protecting the data used during our model training is crucial to ensuring the privacy and security of our clients, as well as complying with international data protection regulations such as GDPR (General Data Protection Regulation).
Below, we explore the technologies and practices we implement at Kriptos to ensure that the data used in training our ML and NLP models is managed securely and ethically. These include anonymization techniques, encryption, and temporary storage, among others.
Data Anonymization: Preserving Privacy from the Start
One of the biggest challenges when training Machine Learning models that process personal data is ensuring that sensitive information remains protected at all times. At Kriptos, we use advanced data anonymization systems to ensure that personally identifiable information (PII) is completely stripped of any links to real individuals.
What is Anonymization?
Anonymization is the process of removing or altering identifiable elements in a dataset, making it impossible to uniquely identify the individuals involved. Unlike pseudonymization, where data is transformed but can still be re-identified with additional keys, anonymization renders the data irreversible, ensuring that no individual can be identified from the anonymized dataset. At Kriptos, our anonymization systems ensure that, before any data reaches our training models, all personal information that could directly identify someone (such as names, addresses, or identification numbers) is removed or altered. This allows us to train models without compromising individual privacy.
Data Encryption: Protecting Information in Transit and at Rest
Encryption is another critical technology we employ to safeguard the data used in our training models. At Kriptos, we use encryption both at rest and in transit to ensure that data is always protected, regardless of where it is stored or how it is used.
Encryption at Rest
Encryption at rest refers to protecting data while it is stored on any system, whether on local servers or in the cloud. At Kriptos, all data temporarily stored for model training is encrypted using advanced encryption algorithms like AES-256, the most secure encryption standard in the industry. This ensures that even if data were compromised during storage, it would remain unreadable and useless to unauthorized parties.
Encryption in Transit
Encryption in transit protects data while it is being transmitted between systems, ensuring it is not intercepted or altered during transfer. At Kriptos, we use TLS (Transport Layer Security) to secure data in transit, ensuring that all information sent over our networks is encrypted and safe.
Temporary Storage and Data Lifecycle
At Kriptos, we understand that limiting data exposure time is key to mitigating risks. That’s why we implement temporary storage for information used in model training. Once the data has served its purpose and been processed, we securely delete it.
Data Lifecycle at Kriptos
The lifecycle of the data we use at Kriptos follows several stages:
- Collection: Data necessary for training our models is securely collected and anonymized before entering the training process.
- Anonymization and Encryption: Once collected, data is anonymized to protect individuals' privacy and encrypted before being stored or transmitted.
- Training: Anonymized and encrypted data is used to train NLP and ML models. During this process, we apply data minimization techniques, using only the information strictly necessary.
- Secure Deletion: After the data is used for training, we implement secure deletion policies to ensure that the data is no longer accessible
Specific Challenges in NLP and ML Model Training
Training Natural Language Processing (NLP) and Machine Learning (ML) models presents specific challenges in terms of data privacy and security. Below, we outline some additional practices we use to protect data at Kriptos.
Use of Synthetic Data
In some cases, to avoid using personal data, Kriptos generates synthetic data, which simulates real data but is not linked to any individual. This data is ideal for training models without needing to access sensitive information.
Continuous Model Evaluation
It’s essential to ensure that ML and NLP models do not accidentally store personal information after the training process. At Kriptos, we continuously evaluate trained models to ensure they do not retain or reproduce sensitive data from the training sets.
Access Control
We implement strict access controls to ensure that only authorized personnel can handle the data used in model training. Additionally, our security policies limit access to the most sensitive data, helping to mitigate potential security risks.
Conclusion
At Kriptos, data privacy and security are fundamental aspects of our NLP and ML model training. By combining anonymization, encryption, temporary storage, and other advanced techniques, we ensure that sensitive data is handled responsibly and securely. This combination of technologies and best practices not only protects personal information but also ensures compliance with the strictest global privacy regulations.
