Does TTN-TSM offer a solution for anonymization of text in translations?
TTN provides an anonymization solution to protect personal and confidential data in documents before translation. Normally, translating sensitive texts (like medical records or legal decisions) with a translation memory is prohibited because it would store personal data that could be accessible to others. If such data were compromised (e.g. via a hack), it could be reconstructed and leaked on the dark web. To prevent this, TTN has developed an anonymization studio that replaces names, numbers, and other identifying details with dummy data or placeholders. This process removes or obfuscates personal information while keeping the document structure intact. Importantly, the original data isn’t lost – it can be reverse-engineered later using a secure key-value mapping file that links each placeholder to the original value. This means authorized personnel can restore the real names or numbers after translation, ensuring accuracy without ever exposing the sensitive data during the translation process.
Which translation projects typically require anonymization?
Anonymization is crucial for any translation project involving personally identifiable or sensitive information. Clients in highly regulated sectors often request this service. For example, medical documents (patient records, lab reports, clinical trial data) and legal texts (court decisions, contracts containing personal details) frequently need to be anonymized before translation. Institutions like hospitals, clinics, courts, and government agencies have strict confidentiality rules and data protection laws to follow (e.g. GDPR in Europe). By anonymizing such texts, these organizations ensure they remain compliant with privacy regulations while still getting the content translated. In short, any document containing names, social security or patient IDs, addresses, financial details, or other private data is a candidate for anonymization to protect individual privacy during the translation workflow. TTN’s anonymization solution is designed to serve these needs, allowing translators to work on the content without ever seeing real personal data.
How does TTN’s anonymization process work?
TN’s anonymization studio uses advanced language processing to identify and mask confidential information before translation. The system scans the source text for any personal identifiers – such as people’s names, company names, addresses, contact information, patient numbers, dates of birth, etc. – and then replaces each with a neutral placeholder or dummy value. For example, a name like “John Smith” might be replaced with “Person A,” or a specific ID number might be replaced with a random dummy number of the same format. These substitutions maintain the same category and format as the original data (so that the text still reads naturally and remains coherent for translation). The key is that no actual personal data remains in the text; it’s all been swapped out for fictitious stand-ins.
Once this replacement is done, TTN generates a key-value mapping file (sometimes called a re-identification key). This file securely stores each original sensitive item and its corresponding placeholder. The anonymized text can then be safely sent for translation (or processed by translation memory systems) without privacy concerns. After the translation is completed, the placeholders in the translated text can be de-anonymized – meaning TTN uses the key file to replace the dummy placeholders with the original names, numbers, and details. This gives you a final translated document that restores all the real information in the right places, but only after the translation work is finished. Throughout this workflow, translators and machine translation engines only handle anonymized data, which greatly reduces the risk of any confidential information leaking.
What anonymization methods does TTN-TSM offer?
TTN-TSM offers three flexible options to perform text anonymization, catering to different security requirements:
- loud-Based AI (ChatGPT): The text can be anonymized using the latest version of an AI like OpenAI’s ChatGPT. In this mode, the content is sent to a cloud AI service which returns an anonymized version of the text along with the key mapping file. While this method leverages powerful AI for high-quality anonymization, it involves transferring the text to external servers (often in the United States). Important: Many Swiss federal agencies and institutions do not allow this mode because sending confidential data to a U.S.-based cloud could violate data sovereignty (due to laws like the U.S. CLOUD Act). In other words, even though the data is anonymized in transit, simply transmitting sensitive text to an American cloud service is seen as a security risk by Swiss regulators.
- Swiss Supercomputer “Alps” (Swiss National AI Model): As an alternative to foreign cloud services, TTN’s system can interface with Switzerland’s own supercomputing infrastructure. The Swiss national supercomputer, called “Alps,” was used to train the country’s large public AI model (known as Apertus – one of the world’s most multilingual open-source LLMs). Through this interface, anonymization can be done using a Swiss-hosted AI model. This approach keeps all data within Switzerland’s jurisdiction, avoiding exposure to the US CLOUD Act. The Swiss model is highly capable (supporting over 1,800 languages), meaning it can accurately identify and replace sensitive terms in a wide variety of languages. Using Alps for anonymization ensures that the data is processed on secure, domestic servers with strict privacy controls. It’s an ideal middle-ground for those who want the power of AI-driven anonymization without handing data to foreign cloud providers.
- On-Premises Model (Apertus 8B): For the highest level of control, TTN can deploy an anonymization model entirely behind your firewall. Specifically, a smaller version of the Swiss open-source LLM (such as Apertus 8B, an 8-billion-parameter model) can be run on local hardware at the client’s site. This means the text never leaves your secure internal network during anonymization – all processing happens in-house. Although a local model might be slightly less powerful than massive cloud AIs, it is still very effective at identifying personal data and it guarantees complete data confidentiality (since nothing is sent over the internet). This option is often chosen by organizations with extremely sensitive data (e.g. defense, top-secret projects, or companies with strict data residency policies). By running the anonymization tool on-premises, you get peace of mind that no external party ever sees the content – it’s a fully self-contained solution.
Each of these methods will produce an anonymized text and a key file. Clients can choose the mode that best fits their security comfort level and compliance requirements. TTN-TSM’s platform is flexible, so you can even start with one method and switch to another as your needs evolve.
Does anonymization allow the safe use of translation memory tools?
Yes, absolutely. Anonymization is what makes it safe to use translation memory (TM) on confidential texts. Translation memory software stores sentences (or segments) from source texts and their translations in a database for future reuse. If you feed raw sensitive documents into a TM, you’d also be storing all the personal data contained in those documents – which is a big privacy no-no. However, if the text is anonymized first, the TM will only store the masked placeholders and their translated equivalents, not the actual personal details. This means you can reap the productivity benefits of a TM without exposing any private information. In fact, data anonymization is a recommended best practice for GDPR compliance in machine translation workflows: the translation memory should contain only anonymized or pseudonymized data, so that nothing in it can identify an individual.
By using anonymization, translators can confidently leverage TM matches and repetitions in sensitive projects without breaching confidentiality. This is extremely valuable for fields like medicine and law, where documents tend to have a lot of repetitive content. For example, medical reports often reuse standard phrases, terminologies, and boilerplate text. With a translation memory, those repeated segments only need to be translated once, and thereafter the TM auto-fills them in future documents. Studies have shown that using a TM can reduce translation time by around 50% on average, especially for texts with many repetitions (such as technical manuals or legal documents). In the medical domain, it’s not uncommon to achieve over 50% cost and time savings thanks to high repetition rates.
In summary: anonymization enables organizations to safely use translation memory and other AI translation tools on sensitive data. It removes the risk of confidential information leaking, all while preserving the efficiency gains of translation memory. This allows you to translate faster and more consistently (by reusing past translations) with full peace of mind that patient names, client details, or other private data will remain protected throughout the process.