Our everyday actions, like buying a morning coffee or taking the train, all create a digital trail of our lives. We as humans tend to fall into individual habits, taking the same routes every day, eating at the same restaurants on certain nights. We create a unique fingerprint through our routine actions. These ‘fingerprints’ make it very easy to predict our next moves.
And with the rapidly growing machine learning technologies, companies are able to predict our next moves.
Here is where transactional data comes in. This data relates to transactions of an organization and includes information that is captured, for example, when a product is sold/purchased. This data is collected from a wide variety of industries, spanning from financial services to transportation, and retail, to name a few.
These collections of information paint a picture of your entire life, online and offline.
How Transactional data is everywhere, in everything.
Transactional data provides a constant flow of information and is necessary for maintaining a company’s competitive edge, deepening client insight, and customer experience.
Each purchase, click, and online movement is held under the umbrella of transactional data. This demographic data, dealing with transactional records, time, or location, all provide access to our real-life behaviors and movements.
Thanks to transactional data, companies can provide customers with a personalized experience. This can be a good thing. For example, banks are a significant participant in growing client profiles using transactional data. Each purchase we do with our bank cards establishes spending patterns. Having AI detect and learn from our purchase habits can help in fraud detection or credit card theft.
However, transactional data constitutes a colossal privacy exposure that is exceptionally difficult to control. For example, perhaps you are someone who Uber’s to work and home. If this action happens only once, it does not represent a significant risk; however, doing every day creates a pattern that can depict several aspects of who you are, where you live, or where you hang out.
Because of this, if a company puts efforts into removing a personal identifier such as a name, it would appear compliant to safeguarding user data. However, these patterns of information can group to re-identify a person without using personal identifiers. An attacker could discover a place of work, a stop for coffee, and a house address without having to know the person’s name.
These extensive collections of our information are not protected to the extent they should be. If companies know and are using such detailed information, how is it not protected to the point of no risk?
Protecting Transactional data
Transactional data will keep growing as IoT becomes more prevalent. As mentioned before, reducing the privacy risk of a dataset that contains transactional data is challenging. It is not just about applying different privacy protection techniques but also understanding how each row relates to each other because the most crucial aspect is to preserve the analytical value.
At CryptoNumerics, we have developed a way to solve this problem. By leveraging CN-Protect and our technical expertise, we are helping telematics companies, as well as companies in the finance sector, reduce the risk of re-identification in their transactional datasets.
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