Social media has become a popular communication platform on which shared content such as images form a large part of the communicated data. Yet, shared images can reveal sensitive information in the sense that the data after its publication remains accessible. Existing studies provide mechanisms to modify co-owned images for user privacy but require that every user involved be online in order to reach an agreement. In cases where users are offline at the time when the image is posted, no privacy agreement can be reached. Having a method of reaching a privacy agreement even when some of the users in the co-owned image are offline is useful in enforcing individual privacy settings vis-a-vis the co-owned image. In this paper, we present a multi-agent negotiation model that enforces individual privacy settings with respect to co-owned images even when the users are offline. Our multi-agent model includes three components, namely a coordinator agent, predictor agent, and filtering algorithm. The coordinator agent collects users’ opinions vis-a-vis a co-owned image to form an image that expresses the opinions of the involved users. The predictor agent supports the expression of offline user opinions, while the filtering algorithm removes privacy-violating information with respect to recent user opinions. Results from our proof-of-concept implementation indicate that improved efficiency in terms of privacy decisions can be achieved by employing agents to support offline user decisions regarding shared content.