This paper introduces a methodology for evaluating PRIvacy LEakage in signature-based Network Intrusion Detection System (IDS) rules. IDS rules that expose more data than a given percentage of all data sessions are defined as privacy leaking. Furthermore, it analyses the IDS rule attack specific pattern size required in order to keep the privacy leakage below a given threshold, presuming that occurrence frequencies of the attack pattern in normal text are known. We have applied the methodology on the network intrusion detection system Snort's rule set. The evaluation confirms that Snort in its default configuration aims at not being excessively privacy invasive. However we have identified some types of rules rules with poor or missing ability to distinguish attack traffic from normal traffic.