From today’s Harvard Business Review:
When collecting consumer data, there is almost always a risk to consumer privacy. Sensitive information could be leaked unintentionally or breached by bad actors. For example, the Equifax data breach of 2017 compromised the personal information of 143 million U.S. consumers. Smaller breaches, which you may or may not hear about, happen all the time. As companies collect more data — and rely more heavily on its insights — the potential for data to be compromised will likely only grow.
With the appropriate data architecture and processes, however, these risks can be substantially mitigated by ensuring that private data is touched at as few points as possible. Specifically, companies should consider the potential of what is known as edge computing. Under this paradigm, computations are performed not in the cloud, but on devices that are on the edge of the network, close to where the data are generated. For example, the computations that make Apple’s Face ID work happen right on your iPhone. As researchers who study privacy in the context of business, computer science, and statistics, we think this approach is sensible — and should be used more — because edge computing minimizes the transmission and retention of sensitive information to the cloud, lowering the risk that it could land in the wrong hands.
But how does this tech actually work, and how can companies who don’t have Apple-sized resources deploy it?
Consider a hypothetical wine store that wants to capture the faces of consumers sampling a new wine to measure how they like it. The store’s owners are picking between two competing video technologies: The first system captures hours of video, sends the data to third-party servers, saves the content to a database, processes the footage using facial analysis algorithms, and reports the insight that 80% of consumers looked happy upon tasting the new wine. The second system runs facial analysis algorithms on the camera itself, does not store or transmit any video footage, and reports the same 80% aggregated insight to the wine retailer.
The second system uses edge computing to restrict the number of points at which private data are touched by humans, servers, databases, or interfaces. Therefore, it reduces the chances of a data breach or future unauthorized use. It only gathers sufficient data to make a business decision: Should the wine retailer invest in advertising the new wine?
As companies work to protect their customers’ privacy, they will face similar situations as the one above. And in many cases, there will be an edge computing solution. Here’s what they need to know.
Read the complete article here.