The Tor anonymity network represents a rewarding target for de-anonymization attacks, in particular by large organizations and governments. Tor is vulnerable to confirmation attacks, in which powerful adversaries compromise user anonymity by correlating transmissions between entry and exit nodes. As the experimental evaluation of such attacks is challenging, a fair comparison of passive traffic analysis techniques is hardly possible. In this work, we provide a first comparative evaluation of confirmation attacks and assess their impact on the real world. For this purpose, we release DigesTor, an analysis framework that delivers a foundation for comparability to support future research in this context. The framework runs a virtual private Tor network to generate traffic for representative scenarios, on which arbitrary attacks can be evaluated. Our results show the effects of recent and novel attack techniques and we demonstrate the capabilities of DigesTor using the example of mixing as a countermeasure against traffic analysis attacks.