In the digital era, people generate a lot of digital traces ranging from posts on social networks, call detail records and credit or debit banks transactions among others. These data could help society to understand different urban phenomena such as what citizens are talking about, how they commute or what are their spending behaviors. Therefore, the use of such data trigger privacy issues. In the present effort, we study four different Statistical Disclosure Control filters to sanitize off-line credit or debit bank transactions. Consequently, we analyze Noise Addition, Microaggregation, Rank Swapping and Differential Privacy filters concerning Disclosure Risk, Information Loss, and utility. We observed that Microaggregation and Different Privacy perform very well for minimizing Disclosure Risk while providing a good utility for statistics of spending amounts per industry type.