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Using deep learning to mitigate privacy risks when analyzing conversational chat data.
Customer interactions on communication channels like social media, SMS messaging, and chatbots generate large amounts of conversational text. This unstructured data contains valuable information about product or service issues, sentiment, feedback, and more; but it may also contain personally identifiable information that is subject to regulatory control. Therefore, access to this conversational text for analysis is often limited or completely restricted in its raw form.
The NLP model classifies free-form text, character by character. The Privitar transformation engine uses the classifications to apply a privacy policy. A policy consists of rules determining how to transform the text, allowing for consistent tokenization of protected information. Privitar trains the NLP model using a proprietary active learning process that enables higher accuracy than competing offerings from cloud vendors. You can tailor the model to your organization’s data.
De-identifying this sensitive data allows you to provision it to a wider group of analysts while upholding customer privacy and ensuring regulatory compliance. Responsible analysis using this valuable unstructured (free text) data source starts with protecting sensitive data that can reveal an identity—such as name, address or location, organization, phone number, email address, credit card number, date of birth, and so on.
You can integrate the Data Privacy for Chat service into your existing data pipelines like Databricks, NiFi, Kafka, and many more. Kubernetes deployment of the NLP model allows you to scale demanding text classification workloads. By deploying the model to CUDA accelerated hardware you can support real-time de-identification.
Privitar Data Privacy for Chat uses natural language processing techniques to recognize and classify text. It then applies policies to de-identify sensitive data at speed and scale. Data Privacy for Chat uses policies to manage the use of conversational chat data. Policies define how sensitive data is masked, how it can be used, and who can access the data in raw or masked form, ensuring safe and compliant analysis. De-identification permits analysis without exposing sensitive, protected information.
Privitar combines a set of de-identification transformations with deep learning models for natural language processing (NLP) to allow users to apply privacy policies on conversational text data.
Review SMS exchanges between customers and service agents to measure effectiveness and efficiency.
Analyze chatbot and live chat interactions for new product and service ideas.
Gauge customer, citizen, supplier, and employee sentiment from conversations on messaging platforms.
Find out how de-identifying sensitive chat data allows you to provision it to a wider group of analysts while upholding customer privacy and ensuring regulatory compliance.
“We want to take the friction out of the processes by minimizing the time spent on data wrangling and ensuring modern data provisioning. The better we are, the faster we can grow our business.”
“De-identification provides an automated and standardized way of removing the identifying values in a patient record across all data collections. It’s not only more efficient but it’s also transparent, so we can improve tracking and auditing of how data is used across the system.”
“With Privitar’s cloud data privacy capabilities ABN AMRO can realize greater value from sensitive data. Greater volumes of data can be made available to users in accelerated timescales.”
“Data exchange is a very exciting concept. It goes beyond governance and compliance and has the promise to allow us to really drive value of the data as quickly as possible, shortening the period tremendously for allowing data to be shared and worked with.”
Our experts are ready to answer your questions and discuss how Privitar’s security and privacy solutions can fuel your efficiency, innovation, and business growth.
