Does TTN-TMS 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 completed, TTN TSM generates a key–value mapping file (sometimes referred to as a re-identification key). This file securely stores each original sensitive element together with its corresponding placeholder. The anonymized text can then be safely transmitted for translation or processed by translation memory systems without privacy concerns. After the translation has been completed, the placeholders in the translated text can be de-anonymized; TTN TSM uses the mapping file to replace the dummy placeholders with the original names, numbers, and details. This process produces a final translated document in which all original information is restored in the correct locations, but only after the translation work has been completed. Throughout the entire workflow, translators and machine translation engines handle anonymized data exclusively, significantly reducing the risk of confidential information leakage.
What anonymization methods does TTN TMS offer?
TTN TMS 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 the client’s firewall. Specifically, a smaller version of the Swiss open-source LLM (such as Apertus 8B, an 8-billion-parameter model) can be operated on local hardware at the client’s site. In this configuration, the text never leaves the secure internal network during anonymization; all processing is performed in-house. Although a local model may be slightly less powerful than large cloud-based AI systems, it remains highly effective at identifying personal data and ensures complete data confidentiality, as no information is transmitted over the internet. This option is typically selected by organizations handling extremely sensitive data, such as defense-related or top-secret projects, or by entities subject to strict data-residency requirements. Operating the anonymization tool on-premises ensures that no external party has access to the content, providing a fully self-contained solution.
Each of these methods produces an anonymized text together with a corresponding key file. Clients may select the mode that best aligns with their security requirements and compliance obligations. TTN TMS is designed as a flexible platform, allowing the anonymization approach to be adapted over time as operational or regulatory needs evolve.
Does anonymization allow the safe use of translation memory tools?
Yes. Anonymization enables the safe use of translation memory (TM) for confidential texts. Translation memory software stores sentences or segments from source texts together with their translations in a database for future reuse. If raw sensitive documents are processed directly in a TM, all personal data contained in those documents would also be stored, which raises significant privacy concerns. When the text is anonymized in advance, the TM stores only masked placeholders and their translated equivalents, rather than the original personal details. This approach allows the productivity benefits of translation memory to be retained without exposing private information. Data anonymization is therefore considered a recommended best practice for GDPR-compliant machine translation and TM workflows, as it ensures that translation memories contain only anonymized or pseudonymized data and do not allow the identification of individuals.
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-based translation tools on sensitive data. It mitigates the risk of confidential information leakage while preserving the efficiency gains associated with translation memory reuse. This approach supports faster and more consistent translation through the reuse of existing translations, while ensuring that patient names, client details, and other private data remain protected throughout the entire process.