| Action | Data Collection Tool | Data Collected | Category |
| Answer WhatsApp messages, an Instagram message, and emails | Whatsapp Instagram Outlook | Time and location of activity Content of messages (?) | Chatting Getting online |
| Ordered Starbucks for pickup | Starbucks App | Time and location of origin, location of pickup | Moving around town |
| Rode the T | T Card | Time and location of departure | Moving around town |
| Lunch at HKS | Credit Card | Time, location, price, and items for lunch | Moving around town |
| Printing | Harvard ID | Time, location, title, number of pages printed | Other |
| Return Library books | Harvard ID | Time, location, books returned | Other |
| Research | Time, location, content of searches | Other | |
| Rode the T | T Card | Time, location of departure | Moving around town |
| Ordered groceries | Amazon | Time, contents, price of order, home address | Moving around town |
| Watch television | Netflix | Time, location, content | Other |
Data Log – February 21
| Action | App / company / entity | Data collected | Category |
| Called my parents | Apple – FaceTime | Call log, time, length of call, location of caller and recipient | Chatting |
| Texted classmates on WhatsApp | 1. Facebook – WhatsApp 2. Google – Gboard | 1. Time, location of messages, message content, images, message read-receipt and viewing activity 2. Keyboard typing activity on phone | Chatting |
| Messaged friends on Messenger | 1. Facebook – Messenger 2. Google – Gboard | 1. Time, location of messages, message content, message read-receipt and viewing activity 2. Keyboard typing activity on phone | Chatting |
| Moved around the city (walking, in a Lyft, etc.) | Google Maps | Background location history | Moving around |
| Bought coffee | 1. Chase 2. Starbucks | 1. Time of purchase, amount spent, vendor information 2. Time of purchase, amount spent, purchase information, credit card information | Moving around |
| Took a Lyft to the airport | 1. Lyft 2. Chase | 1. App interaction information (button clicks, swipes, time spent on app), pick-up location, destination, time, credit card information, driver and car information, the route I took 2. Time of Lyft, amount spent, Lyft vendor information | Moving around |
| Checked in to a Delta flight to NYC, went through security, and boarded flight | 1. Delta 2. TSA (U.S. Government) | 1. App interaction information, personal ID information, TSA number, flight information 2. Flight information, check-in information | Moving around |
| Ate dinner at a Mexican restaurant | 1. Restaurant 2. Chase | 1. Time of purchase, purchased items, credit card information 2. Time of purchase, amount spent, vendor information | Moving around |
| Checked the weather app | Apple | App interaction information | Moving around |
| Connected to WiFi | MIT | Device information, connection times, data sent through network | Going online |
| Watched Netflix | Netflix | Time, location, viewing content and browsing activity | Going online |
| Did research for thesis | 1. Google 2. MIT | 1. Search terms, browsing history 2. Browsing activity within MIT resources | Going online |
| Listened to podcasts | 1. Apple 2. WNYC | 1. App browsing and listening activity, timing 2. Podcast download and listening activity | Going online |
Patricia’s Data Log 2/23
Chatting with People
I was in constant communication with people over WhatsApp and IMessage throughout the day, including sharing a playlist I made on Spotify with two people who then both used it to run – which I’m sure Spotify noticed. Around 2 I logged into the MyFitnessPal app to enter what I’d eaten in the day, and was prompted to allow location access, which I said yes to without thinking, and then immediately paused to wonder why a nutrition app needs my location – so I went back into my app settings and turned off its location access, but at that point it would’ve already located me at least once today. I also checked the weather app frequently, which tracks my location.
Moving Around Town
To start, I wear a Fitbit every day, so my walk to my friend’s home for breakfast, my run, and my errands were all tracked, along with my location and heart rate.
At 2:30 I went for a run, and using Strava to track the run which absolutely takes my location/ route and stats, and listened to a playlist on Spotify, which I always listen to when I run, and which I know Spotify stores the statistics on (my annual roundup of most listened to music on Spotify is always a carbon copy of my workout playlist). On the run, I would’ve been recorded on multiple cameras, as I ran by MIT’s campus and multiple banks and retail buildings.
Later in the day I walked to Whole Foods for a few ingredients for dinners for the week, and paid using my credit card, which allows Whole Foods to identify my and add my purchases to their profile on me.
Around 8pm I then realized that I needed quick oats for a cookie recipe, so I ran out to the convenience store on the corner, which has security cameras that recorded me, and where I paid for my purchases with my credit card.
Getting Online
I googled the results of the Nevada caucus in the morning, and then immediately followed that with a google about what the process of the Nevada caucus is, which other states have caucuses instead of primaries, and then what the schedule of the next few primaries/caucuses are.
I checked the Data Storytelling website midday, including logging into WordPress, to get a sense of how to write this blog post, and then logged into my Outlook email, which I’m sure Microsoft timestamped/recorded. I then logged into google calendar to add several events to my week based on emails I was checking.
While I was cooking dinner, I logged into my sister’s amazon prime account to watch a show, which would have logged what I watched and for how long, as well as possibly my location(?) – I’m fairly sure amazon must know it was me and not my sister. I then also looked up a recipe I was following on a blog, and then re-googled the Nevada caucus, and results were still being reported.
Other Things
I wear a Fitbit every day, so my walk to my friend’s home for breakfast, my run, and my errands were all tracked, along with my location and heart rate.
Chatting with People
I woke up for the day and couldn’t find my phone. I used my partner’s phone to Find my iPhone, so my location at the time was the same as his. When I found my phone, I sent a brief text and responded to emails.
Moving Around Town
I spent the morning walking around Manhattan, so I could be tracked via surveillance/traffic cameras. I used apple maps to navigate to Rockefeller Center, so my phone knew my location and the pace at which i was walking. I took the subway back home, so my location could be tracked by the stops I got on/off at.
Getting Online
I used instagram and facebook throughout the day, so my phone could keep track of the times I was most likely to be on/off my phone, and what kinds of content I spent time looking at.
Other Things
I bought snacks at a Hmart in the evening using my credit card. In the evening, I went to a bar and used cash, but i definitely checked social media at the bar. For dinner my partner used his name to reserve a table for two, so it’s possible that I could be traced to that location.
Robert V’s Data Log for Feb. 21st
Chatting with People
I use a few apps daily. These are mostly regular phone messaging, WhatsApp, and Messenger. These apps have to store the messages that I send to other people (or at least move the messages from my phone to the other people’s phones), which creates insecurities both in storage and during transmission. Even if there is an end-to-end encryption, the event that “Robert sent some web request somewhere” is still potentially logged.
Moving Around Town
At MIT, we have to tap our card in many of the buildings we enter, especially dorms; in addition, many of those areas have cameras. This means that it can be fairly easy to track people.
I also have some friends who wrote an app that could detect high traffic areas using wifi signals. Just in the way wifi work (by probing wifi routers literally in all directions), the information that “I disconnected from wifi X and connected to wifi Y” can also be used.
I also worked on a project to design a system that could facilitate indoor tracking using bluetooth. So, I wouldn’t be surprised if someone could combine all these signals to be able to track people.
Getting Online
There’s a game I play almost everyday (and I should stop…). That game logs pretty much everything, which in turn it uses to award coins or rewards. That’s a form of tracking. The game has in app purchases, which can be used as a proxy to discover someone’s financial status.
Other Things
I think that cellular network is the least secure and most dangerous of all, including SMSs. We’re all kind of stuck to using it because we carry our phones everywhere. Now that phones don’t have battery removals, it’s almost impossible to know whether your phone is actually off even when you turn it completely off. This video from the Joe Rogan Experience with Edward Snowden is particularly telling.