With Other Rave Users
On Rave, you represent yourself to other users with your username, handle, and profile picture which are publicly available.
Of course, all chat messages and voice sent over Rave are shared with users in the same Rave.
With permission, your approximate location, derived from your IP address and not from your GPS, is visible to others on the map screen.
With Third Parties
We may share information about you as part of a merger or acquistion.
If Rave gets involved in a merger, asset sale, financing, liquidation or bankruptcy, or aquisition of all or some portion of our business to another company, we may share your information with that company before and after the transaction closes.
We may share information about you as part of a valid legal process, government request, or applicable law, rule, or regulation.
We may share information with other service providers who perform services on our behalf, including to facilitate payments, measure and optimize the performance of ads. We also derive and share market verticals based on watch history for monetization purposes including to improve the quality of ads you receive.
When using Rave for the personal computer, we may share your information with the third party Freestar.
Rave is affiliated with Freestar for the purposes of placing advertising within the app, and Freestar will collect and use certain data for advertising purposes.
To learn more about Freestar's data usage, click .
All or partial advertising on this Website or App is managed by Playwire LLC.
If Playwire publisher advertising services are used, Playwire LLC may collect and use certain aggregated and anonymized data for advertising purposes.
To learn more about the types of data collected, how data is used and your choices as a user, click .
We may share information about you to investigate, remedy, or enforce potential Terms of Service and Community Guidelines violations as well as protect the rights, property, or safety of us, our users, or others.
Google Workspace APIs are not used to develop, improve, or train generalized/non-personalized AI and/or ML models.