Free of charge applications and services are a beloved and growing matter on the internet. Users expect services on their mobile phone or on the world wide web to be free. What most users do not realize: they often give their personal information to the provider in return for these allegedly free of charge offers by using the services. Companies doing so-called data-based business models are specialized in collecting and selling personal data for secondary data use, e.g. Facebook, Google, Spotify, Shazam. Research shows, that a rational decision often cannot be made by the users as they often do not know or care and cannot follow up what is happening to the data. But the user needs to assess if it is safe and worth it to give his/her user information to service providers on the internet. Privacy Calculus Theory proposes that an individual’s intention to disclose personal information is based on risk-benefit analysis. Meaning that users are comparing the risks and benefits of providing their personal data before doing so. Very often this risk assessment is biased by various factors, e.g. users only see the benefits but black out the risks of providing personal information to providers. Several research has been conducted to identify biases infecting consumers’ risk assessment in general, but very little research on the effects of biases on risk assessment in Privacy Calculus. Also, there has been identified a lack of research on the impact of BNDE on biases. This is of particular interest in the current debate about information privacy in online services. This work will investigate which biases occur in risk assessment in Privacy Calculus. How biases affect risk assessment in Privacy Calculus. And how BNDE does affect a bias. This master thesis will transfer biases from general risk assessment onto risk assessment in the Privacy Calculus, test it by qualitative research and investigate the impact of BNDE on these biases.