MoiPrivacy: Design and Evaluation of a Personal Password Meter

Passwords commonly contain personal information. However, there is limited awareness about its detrimental effect on the user’s online security. Current password meters do not take into account personal information and, therefore, their users are susceptible to targeted password guessing. In this paper, we present the MoiPrivacy password meter, that extends a neural network- and heuristic-based approach and considers a user’s personal information, while calculating the password strength and feedback. To do so, we analyzed the type of personal information used in passwords through an online survey (n = 62). We conducted a second user study (n = 49) for evaluating the MoiPrivacy browser extension. Our results show that MoiPrivacy significantly limits the inclusion of personal information in passwords.