Machine learning has many advantages ranging from the ability to easily identify patterns in data sets to automating processes. It isn’t surprising to note that the market is expected to have a CAGR of 39%. The value of machine learning in the global market was approximated at $8 billion in 2019. By 2027, it is expected to reach $117 billion.
Typically, Machine Learning (ML) models use data that is publically available. It allows a computer system to study concepts and patterns to make predictions for new situations. However, when it comes to industries such as finance, identity verification, etc., the data is highly sensitive and ML presents several data privacy challenges. Simply removing part of the data may not be enough to mask an identity as ML tools can pick up subtle nuances in other fields to infer the individual’s identity.
Companies working with sensitive data need to comply with a variety of regulations. The good news is that it is possible to design a workflow that keeps data protected while allowing machine learning engineers and data scientists to develop predictive models and experience the benefits of machine learning. Here are 8 few tips.
1. Control Data Access
When working with sensitive data, it is imperative to control who can and cannot access information. You need to balance protecting the customer’s privacy with giving ML engineers and data scientists the flexibility to work.
The foundation to controlling data access lies in providing a physically secure environment. The computers where sensitive data is stored or from which the data can be accessed must be housed in a special room that does not have public internet access. This room should not have any transparent windows and use cameras and biometric readers to control entry to the room as well as multi-factor authentication.
2. Maintain Access Logs
In addition to restricting data access, you will also need to maintain a record of the people accessing the data. These logs need to mention who accessed the images and metadata, when it was accessed and from where the files were accessed.
ML engineers and data scientists accessing the data cannot be permitted to create copies of the data on their phones, laptops or any other form of external storage. Data cannot leave the secure room under any circumstances.
3. Encrypt all Data
Encrypting datasets is not just a practical need for Machine Learning that involves sensitive data but also a requirement to comply with audits such as the ones conducted by the PCI-DSS certification authority.
Data encryption is required for data at rest as well as data in transit. It would be disastrous for a customer’s personal data to become public just because the data was decommissioned and sent over the wire without encryption.
4. Define Dataset Retention Periods
While data availability is critical to Machine Learning models, data cannot be stored indefinitely. Especially if you are dealing with sensitive data, you must define a period for which the data can be stored. This is important as derived data can often potentially contain customer’s personally identifiable information (PII). For example, a cropped image may contain part of a customer’s home address that puts their identity at risk.
To keep this data from falling into the wrong hands, you must define dataset retention periods. Once the data retention period is over, the data and all derived data must be destroyed
5. Get Client Consent
All data being used for ML must be acquired by getting client consent. This is important for data security as well as compliance with regulations. Traceability must be maintained from the moment information is entered into the datasets.
When talking of customer consent, it is important to check whether the customers give their consent to using the data for training models that will be used only for them and not other customers or sign off to having their data used to train models for everyone.
Despite giving their consent initially, customers may decide to take back consent at any time. When a deletion request is made, it must be processed at the earliest. This means that all data must be tagged and organized in a way that it becomes possible to delete all traces of the customer’s data.
6. Monitor Online Databases
For ML models to be reliable, the data being used to train them should be up to date, accurate and complete. For this, the data sets need to be updated regularly by comparing them to data stored in reliable third-party databases such as the national ID and passport records.
When data becomes obsolete, it must be updated so that the ML model results remain reliable. Monitoring online government databases for data and identity verification also helps detect concept drifts and protects the ML models from fraudsters.
7. Create a Secure ML Model Development Environment
When you’re dealing with sensitive data you need to decouple the software development life cycle and the ML model development. You will need distinct development and staging environments with centrally managed data access to keep data safe while ensuring reliable computational outputs.
The development environment is the ideal platform for ML engineers and data scientists to test new ideas. Any data entering this platform must be provided with the customer’s consent.
In the staging section, ML engineers can run silent workflows as alternatives to the production workflows. This involves real production data which allows the output of both workflows to be compared without any risk of data corruption.
8. Regulate Data Quality
With the sensitivity of data in mind, it becomes imperative to maintain high-quality standards for the database. All data used in the ML models must be complete, accurate, timely and correctly formatted. While no copies can be created, duplicate data records can enter the system. By continuously monitoring the database for quality checks, you must filter out duplicates and merge records to create single, unique records for each customer.
By regulating data quality, you can ensure that the results of the ML models are reliable.
In Conclusion
Machine Learning has the capability of increasing productivity and extracting more value from datasets while reducing operational costs. For industries that deal with sensitive data, the workflow needs to be balanced with efforts to protect customer privacy. Acquiring customer consent for all the data used is essential to achieving this.
ML engineers and data scientists must be very careful with how they store and access data. Maintaining high standards of data quality is key to the efficacy of ML models. Once verified all data must be encrypted and retained for defined periods. Further, to use this data, separate development and staging environments will need to be maintained.
By following these tips, ML engineers can tap into the opportunities provided by Machine Learning and revolutionize the way things are done.
