Exploiting personal identifying information (PII) is critical for secure access to digital and web-based systems, it is also a significant element of the online social media business model. However, how this exploitation relates to users’ valuation of their PII is poorly understood as an individual’s willingness to disclose items of PII in different situations is unknown. For instance, an individual may delight in accessing their smartphone using facial recognition, yet they may hesitate when accessing banking services or vice versa. Moreover, the actual cost of disclosure gets obfuscated within dense and lengthy policies in a manner designed to exploit additional data. Thus, an individual may not understand that systems such as facial recognition can be a gateway to infer further PII.Even with respectful intentions, identity-dependent technologies face a myriad of challenges to transparently balance users’ sensitivities with their own need for high veracity PII. In a novel application of the ELO ranking algorithm, we detail a frugal and scalable method of capturing and combining some of these sensitivities. The design involves a set of 33 items of PII, and a cohort (N = 115) divided into three contexts: expression (35), transaction (40) and submission (40). The results indicate that while individuals may have many differences, as a cohort the personal utility of PII still collates and forms distinct clusters of PII that relate within and across contexts. This result means that technologies that treat PII as one amorphous group, and those transferring PII across contexts, risk failing to adhere to the sensitivities of the user. However, by working with these cohort-based clusters in mind, it is plausible that system designers and policymakers may better appropriate system needs with the wants of the individual.