24 May 2022 | by Marc-Elian Bégin
In poker rooms around the world, players keep their cards close. It’s the only way to avoid prying eyes. There’s an important lesson here for organisations collecting data, video and audio — these inputs are your cards and absolutely no one else should see them.
The wealth of information from your sensors, cameras, microphones, actuators and other new digital sources should yield valuable insights without intrusion.
When talking with customers, partners and prospects, I’m surprised by how frequently the topic of privacy intrusion comes up, and this is usually part of a broader conversation around the importance of data sovereignty. In Europe, these discussions typically reference the data privacy and security law, the General Data Protection Regulation (GDPR), while in the Americas and further afield, other reference points are used to understand and interpret the laws around digital privacy.
What can you see?
As the cost of easy-to-deploy digital source equipment is going down, technical capabilities are going up, so you can understand, see and hear much more from your input streams than ever before. For example, today’s latest video cameras deliver extremely high levels of definition. This is great for generating precise insights on, say, product quality or behavioural analysis – information that’s very valuable to your organisation and to others.
No-one else should be able to see your information, or that of your customers. There are onerous standards and regulations around the operation and management of surveillance equipment, including closed-circuit television (CCTV). These requirements represent such a burden with which most organisations are unwilling or unable to comply. But without adequate protection of the information you gather, there is a very real and growing risk of someone snooping on you.
A key promise of edge computing is that instead of sending your data, video and audio to the cloud to be processed, you keep it close. All of your input streams are processed locally, near the digital device that generates them. Here’s what you gain:
Higher-quality insights: When streams are processed in their entirety near to source, they yield the best possible results. But when streams are transported over the network to the cloud for processing, their quality is normally reduced and this can result in degraded insights.
Speedier insights: Your time to insight is faster if you process streams locally – this can be done in real time and without any delays or round trips. Instead, sending streams over the network can cause perturbations and delays, especially if connections are weak and bandwidth is limited. Also as video and audio definition increases, data size increases and journeys to the cloud take longer.
Easy data fusion: Because your data is available at the edge, analysis can be easily performed here on all different categories of data (video, audio, etc). An example is analysis both of the sound of a noisy vehicle and images as the vehicle passes, showing its brand, model and number plate. Data fusion such as this enables more powerful analytics – in this case, it could mean better predictive maintenance or regulation enforcement.
Tighter privacy: The excellent news is that all the above are achieved without privacy intrusion. Why would you want to move, process and store privacy-sensitive data in the cloud – which, by the way, additionally requires designing and managing a whole layer of cloud privacy applications? Instead with edge computing the same information stays local and is stripped from any data that might intrude on citizen or corporate privacy.
Better control: With edge computing, you call the shots. A well-managed edge computing solution puts you in remote control, able to update the devices and apps running at the edge, aligned with your changing needs. So as your organisation evolves, so too can your edge computing strategy.
At SixSq, we have a wealth of experience in privacy protection. So contact us today to discuss new ways of stacking the cards in your favour.
This post was first published on the EdgeIR website.
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