Personalization vs privacy
In exchange for utility and personalized experiences, users provide personal data to applications. Users know that their data can be used against them but the extent of which is oftentimes underestimated.
This article will show how Telegram’s “People Nearby ” function can be used to monitor user movement.
Location Information
Deriving Telegram user’s location with triangulation
Spoofing the analyst’s device location to a different reference point will give the analyst different “distance readings” from the user on Telegram. The location that satisfies each relevant distance from the reference points is the estimated location of the telegram user. An easy way to do this would be to draw a circle with a radius of (x KM away from telegram user) for each reference point. The intersect of the circles would be the estimated location of the telegram user.
Time-series location data
The above process when performed periodically produces time series location data of the telegram user.
The above visualization allows the analyst to monitor the telegram user’s movements. Information such as “duration spent in a location” and “time of day” allows the analyst to easily classify locations into categories such as Home or Work. With sufficient data, regression models can then be used to predict the future location of the telegram user.
