on the topic of the GUID that is you, part 4

we’re winding-up our discussion of big-data collection as prompted by a recently released federal trade commission report; the links to all previous parts are at the bottom.

 

when i was a kid i lived in a dump. a trashy neighborhood in an unincorporated burb of louisville, ky; my mom called it a ghetto. i kind of knew what that meant because without a lot of supervision, i watched tv all the time (70’s sitcoms and the like) and ran around with a hooligan crowd. i was 10.

where i wanted to live was with the brady bunch family. they had a cool house. the jetson’s, of course, had the perfect place but that was a cartoon; everything was connected and the gadgets were out of this world. ironically – and flying cars aside – we’re closer to a jetson-esque home than a brady one. i mean come on: six siblings that get along, an uncontroversial and supportive maid, a mom & dad the kid’s thought were hip, and all living a middle-class dream? that’s leave-it-to-beaver land man. again, i digress.

the jetson’s had a wired home. imagine… you have a tablet appliance no bigger than an ipad or smartphone. it’s connected to your wifi and receives wireless input from every electrical outlet in your home, every major appliance, every tv and audio/video device, home alarm system, thermostat, door locks, garage door, pool/spa filter… everything.

a few years ago, intel was noodling with such an appliance prototype and i designed the data model for it. they called it the “Intel Intelligent Home Energy Management System” but the scope of my data model allowed it to do more than manage home energy — a lot more. more with oak leaf clusters type of more. here’s a list of some of what my data model supported:

  • electric power consumption at the device level
  • hvac (heading, ventilation, air conditioning)
  • pool controllers (pool & spa systems)
  • security controllers (home security systems)
  • entertainment systems (audio, video & gaming systems)
  • health systems (medical device monitoring & control)
  • distributed generation (on-property power generation from solar, wind, back-up generator)
  • fire & sprinkler systems (alarms and suppression)
  • water systems (municipal and other)
  • telephony (cellular, voip, and land-line)
  • egress (doors and windows)

pretty slick huh? the technology envisioned for this appliance exists now just as it did a few years ago; it’s just improved. i own a nest thermostat that emails me a usage report each month; this isn’t rocket science either. so what happened to intel’s prototype and why can’t we buy this from amazon? the answers are, respectively… i’m not sure and you can (look here). but that’s for someone else to write about. this is about the data.

and by data i mean everything! someone looking at the data could know not just the room where something was but its exact location on the globe in an x/y/z coordinate space. associate meta-names to that space (e.g. judy’s room, george and jane’s b/r, elroy’s bathroom, etc.) and the picture of a wired home blossoms in vivid precise detail.

is a light on in the basement… which one and for how long? is someone taking a shower in the master bathroom? what temperature is the water? who turned off the alarm or unlocked a specific door? was the oven left on? did someone swim in the pool? are the curtains over this window drawn? what date and time did it happen? my model allowed occupancy censors to record how many people were in the house and in what room.

but even without that functionality, i could analyze the collected data and tell you, with 98% certainly, which family member was home and what they were doing. using trending analysis over time, i could predict almost anything about what and who was going to happen in that home on any given day.

with access to the real-time data stream (not just the post-event stored data), i could tell you who was doing what at this precise moment. if you think a data broker inferring my marital status (see part 3) was intrusive or spooky, these capabilities should blow your mind. and all the news is about the nsa knowing who you called; go figure.

wired homes are envisioned to enhance our lives through automation and situation awareness (is your garage door open?), and i sincerely believe the intentions of high-tech visionaries are credible. as with many things, however, unique new threats tag along with unique new opportunities.

before my term had ended on the project with intel, i communicated these concerns to people with pay-grades higher than my own. but, because my role and title was not chief-skynet-avoidance-officer, the implementation of data safeguards was outside my purview. who watches the watchers?

this bigger than buying dryer sheets are ralph’s or placing an order for toner with amazon. the data – as data objects go – is similar on a conceptual level but the implication is much greater.  should i ever consider living in a wired home, you can safely presume i will take greater interest in what data is collected, where it goes, and who sees it; much more than i care about my generic shopping interests.

otherwise i’ll just have to quote george jetson: Jane! Get me off this crazy thing!

 

 

 

this is part 4 of a multi-part series:

 

 

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