Online Social Networks (OSNs) have become one of the central platforms for communication, content sharing, and digital interaction. However, their widespread use raises important concerns regarding the exposure of personal information and the economic value of users’ data. While previous research has examined privacy risks and data leakage mechanisms in OSNs, relatively little work has quantified how users value their leaked personal information. This study investigates how users assign monetary value to different types of personal data in OSN environments by proposing an experimental framework that estimates privacy valuation through observable user decisions. To this end, an interactive simulation was developed in which participants received incentives for disclosing specific personal information under different contextual conditions. The framework considers four factors: the type of personal data requested, the platform where the data would be exposed, the object offered as an incentive, and the timing of the incentive. The empirical study involved 249 participants. The results reveal substantial variation in how users value their personal data. Financial information, such as bank account details, was associated with the highest privacy valuation, while disclosure behavior was influenced by platform context and the value of the offered incentive. High-value or socially sensitive products increased participants' willingness to disclose information, whereas low-value incentives had minimal influence. These findings demonstrate that privacy valuation in OSNs, specifically of leaked data, is highly contextual and influenced by both economic and social factors. The proposed framework contributes a practical method for estimating the perceived cost of personal data exposure, and offers insights that may inform the design of privacy-aware systems, consent mechanisms, and data governance policies..