Privacy concerns can also rise during the ride as some drivers choose to use passenger facing cameras for their own security.
[1] However, in these telephone-based programs the operational costs started exceeding their revenues and an alternative, internet and email driven ride-matches, was proposed.
This program was tested on a closed campus (it was only available to people associated with the University of Washington), which proved highly successful.
Websites originally had lists or forums that people could get information for carpooling options from, but the internet provided the ability to develop platforms, which were more dynamic and interactive.
Since carpooling and ride-sharing were not very popular options, the smaller population who did participate already had set agendas, so timing-wise it was not helpful to those who needed transportation outside of a regular workday commute.
"[4] According to the Omnibus Crime Control and Safe Streets Act of 1968, there are policies regarding recording audio conversations, including clarifications about the "one-party consent" rule that comes with it.
[5][6] Corporations can collect information on what types of stores and what brands are most often visited by a user and can build an online profile, which is traceable.
This can also relate to advertising companies, which can target personal interests and alter their online interactions to start showing ads that are catered and specific towards where the user has visited.
This can apply to medicinal, religious, or legal affiliations as well, that a user's location and places visited cannot be justified when being looked at from an outside perspective.
However, while this is a very basic level of deflection, putting a home address a couple streets away still gives a general location of where the user is stationed.
Mobile sensing is the process of pinpointing the user's physical device, which has sensors and information that can be collected.
While there is an added level of security, such as passcode or touch ID before every transaction, this does not ensure the safety of this information in the app.
However, this can cause an issue because if somehow a rider's image is saved and uploaded to the web, connections can be made to personal accounts.
For example, with Facebook's face recognition advanced algorithm, it is easier to identify people's identities from outside pictures.
Researchers have come up with a conclusion which introduces a solution for these issues which is a system that helps with both data privacy and user anonymity.
The paper proposes a solution, anonymization, which protects user's data in case of accidental breaches.
There are data tables that show experimental distances of how close a tracking software was to those who had implemented the fuzzy solution.
There are several mechanisms proposed that would be helpful in hiding data including location obfuscation, perturbation, confusion and suppression, and cryptographic techniques.
There are a couple issues that arise with this algorithm, including determining how much noise should be implemented and if the changing of the data is enough to alter it to an unrecognizable form from its original state.