In his newest book, The Every, Dave Eggers satirizes our fascination with personal surveillance technology and reminds us of the endless ways we readily, willingly, happily, and eagerly relinquish freedom and privacy in order to be connected. So, when I recently learned that Amazon is piloting a Whole Foods grocery to be almost completely run by tracking and robotic tools, I got the heebie-jeebies. What else in our lives captures our attention under the premise of convenience, only to require us to surrender data about our everyday decisions?
Is cleantech joining the trend? When will it become commonplace to optimize energy yield via smart tracking? Will users rise up and demand privacy protections? What are important theoretical and practical implications of preserving user privacy in cleantech R&D?
I don’t have the answer to those questions, and maybe you do. I do know that research into “cleantech data collection 2022” is a little unnerving.
Let’s start with the Amazonification of Whole Foods, as the New York Times termed it. A pilot program at Whole Foods makes it the most technologically sophisticated grocery store in the US. It has two options: you can scan either the QR code in your Amazon app or place both hands on a kiosk. Either links to your Amazon account.
If you choose the hand recognition option, you’d hold you palm over the turnstile reader to enter. Then, as you shop and pick up various items from the grocery store shelves, cameras and sensors record each of your moves. The technology used for this real/virtual shopping experience is called Just Walk Out and consists of hundreds of cameras. “Those are the cameras that will follow you during your shopping experience,” a Whole Foods employee explained. Hundreds of small black plastic boxes hung overhead from the rafters.
Sensors are placed under each item available for purchase. You want a carton of almond milk? Pick it up, and the sensor awakens and takes note of your purchase.
But the automation doesn’t stop there — your entire haul is itemized, and the total is charged when you leave the store. No cashier. Anyone with an Amazon account, not just Prime members, can shop this way and skip a cash register, since the bill shows up in your Amazon account. An email from Amazon appears in your inbox just a brief amount of time later, noting the duration of your shopping experience and total amount of purchase.
Behind the scenes, deep-learning software analyzes the shopping activity to detect patterns and increase the accuracy of its charges.
It’s paradoxical: you’re assembling items in a virtual shopping cart, but you’re live, in person, here-and-now.
Alex Levin, 55, an 18-year resident of Glover Park, told the New York Times that people should not reject the store’s changes. “We need to understand the benefits and downsides of the technology and use it to our advantage,” he said.
“It’s like George Orwell’s ‘1984,” Allen Hengst, 72, a retired librarian, countered.
Cleantech, Data Collection, & Privacy Issues
Data for a company refers to every crucial information the company possesses about its customers, market insights, and even its competitors’ marketing approaches. All these data are analyzed and worked upon to develop strategic decisions. Then there’s Big Data, which are taken to the next level with advanced analytical techniques.
Now I’m not saying that all cleantech data collection is bad. For example, the Clean Technology Data Strategy (CTDS) provides information to measure the economic, environmental, and social contributions of the cleantech sector in Canada to track and understand Canada’s contribution to clean growth and the transition to a low-carbon economy. It provides environmental solutions to such issues as climate change, air and water pollution, and resource scarcity.
What I’m talking about is more like research that starts with data points that become extrapolated outward through qualitative judgments, from insight from investors and executives from corporations and industry active in technology and innovation. About experts who have been gathering hundreds of petabytes of data (one petabyte equals a million gigabytes) related to the natural world and society. They muse among themselves, “Imagine the possibilities.”
Gives me the shivers.
The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Especially, some common algorithms in IIoT technology, such as deep models, strongly rely on data collection, which leads to the risk of privacy disclosure.
Using the example of smart grid systems, data security is a serious concern because the systems are created through linking a vast number of IoT devices. These devices produce an enormous volume of data that are kept in cloud storage and transferred over different networks. In recent years, rapid advancements in smart grid technology and smart metering systems have raised serious privacy concerns about the collection of customers’ real-time energy usage behaviors.
A smart grid produces tremendous quantities of data from a range of intelligent devices that need to be managed in real time for purposes such as optimizing energy flow, operations management, system monitoring, and production decisions. The data may include client data, business data, power system data, location data, weather data, and so on. There may also be confidential and sensitive client and stakeholder data, as well as information about a country’s central power grid.
How often are data collection companies incorporating privacy-preserving data aggregation models? Less frequently than is probably wise, that’s for sure. Ensuring security and privacy is a major challenge because smart grids are vulnerable to numerous threats including data stealing, denial of service attacks, and malicious data injection, among many others. There’s also the inherent temptation of companies to instigate punitive measures if an individual’s data reveals outlier tendencies or aberrant energy usage.
Cleantech Startups & the Tendency to Surrender Data
Increased investments in environmentally sustainable startups can contribute to the transformation to a more sustainable economic system, sometimes called a “green revolution.”
Cleantech startups have a valuable place in the broader sustainability transition. We also know that many new cleantech companies enter the marketplace as fragile startups. To become established and reputable, they likely draw upon an agent-based model developed to simulate emerging adoption behavior in a community, and that simulation is derived from data. Given the data collected is drawn from a randomly selected sample, its analysts assume that the sample’s choices and attributes are likely to be representative of a particular community.
The technology adoption behavior of this hypothetical community is predicted based on planned behavior, utility maximization, and social network analysis. Corporate venture capital investors, in turn, become intimately familiar with each potential cleantech startup as a stand-alone entity through the data they generate.
All this data floating around in cyberspace means that our cleantech behaviors that seem private are rarely so.
If we return to the case of Amazon, we can unpack the variety of data the company drives from its commercial ventures to draw data analogies: Alexa Voice Recordings, a personalized recommendation system based on behavioral analytics, one click ordering, anticipatory shipping, and book recommendations. From those flash points, the company increases efficiency through supply chain optimization, price optimization, fraud proactive methodology, and personalization proposals to clients across almost any area.
The Guardian noted last week that individuals who have requested their data from Amazon are astonished by the vast amounts of information they are sent, including audio files from each time they speak to the company’s voice assistant, Alexa. A Wall Street Journal investigation found that Amazon uses data from third-party sellers to help develop its private-label goods. Among the findings were that some Amazon executives had privileged access to data on individual third-party sellers, which was then used to develop the company’s own products, despite it being in violation of company policy.
Data analysis has transformed Amazon into the biggest e-commerce site in the world. Will cleantech follow suit?
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