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Home arrow Computer Science arrow Designing Data-Intensive Applications. The Big Ideas Behind Reliable, Scalable and Maintainable Systems


As a thought experiment, try replacing the word data with surveillance, and observe if common phrases still sound so good [93]. How about this: “In our surveillance- driven organization we collect real-time surveillance streams and store them in our surveillance warehouse. Our surveillance scientists use advanced analytics and surveillance processing in order to derive new insights.”

This thought experiment is unusually polemic for this book, Designing SurveillanceIntensive Applications, but I think that strong words are needed to emphasize this point. In our attempts to make software “eat the world” [94], we have built the greatest mass surveillance infrastructure the world has ever seen. Rushing toward an Internet of Things, we are rapidly approaching a world in which every inhabited space contains at least one internet-connected microphone, in the form of smartphones, smart TVs, voice-controlled assistant devices, baby monitors, and even children’s toys that use cloud-based speech recognition. Many of these devices have a terrible security record [95].

Even the most totalitarian and repressive regimes could only dream of putting a microphone in every room and forcing every person to constantly carry a device capable of tracking their location and movements. Yet we apparently voluntarily, even enthusiastically, throw ourselves into this world of total surveillance. The differ?ence is just that the data is being collected by corporations rather than government agencies [96].

Not all data collection necessarily qualifies as surveillance, but examining it as such can help us understand our relationship with the data collector. Why are we seemingly happy to accept surveillance by corporations? Perhaps you feel you have nothing to hide—in other words, you are totally in line with existing power structures, you are not a marginalized minority, and you needn’t fear persecution [97]. Not everyone is so fortunate. Or perhaps it’s because the purpose seems benign—it’s not overt coercion and conformance, but merely better recommendations and more personalized marketing. However, combined with the discussion of predictive analytics from the last section, that distinction seems less clear.

We are already seeing car insurance premiums linked to tracking devices in cars, and health insurance coverage that depends on people wearing a fitness tracking device. When surveillance is used to determine things that hold sway over important aspects of life, such as insurance coverage or employment, it starts to appear less benign. Moreover, data analysis can reveal surprisingly intrusive things: for example, the movement sensor in a smartwatch or fitness tracker can be used to work out what you are typing (for example, passwords) with fairly good accuracy [98]. And algorithms for analysis are only going to get better.

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