## 4 Experiments and Results  
In the present section, we describe the results of applying different measures to quantify the information loss (Section 4.1), disclosure risk (Section 4.2), and utility (Section 4.3) of the differential private sequential pattern and _DP-FSM_ algorithms for three different datasets, which are described in the following paragraphs:  
1. **Statlog** or **German Credit Data**: This dataset was provided by Prof. [PERSON]. It contains categorical attributes to clients having good or bad credit risk (German Credit Data: [[https://archive.ics.uci.edu/ml/datasets/statlog+](https://archive.ics.uci.edu/ml/datasets/statlog+)]([https://archive.ics.uci.edu/ml/datasets/statlog+](https://archive.ics.uci.edu/ml/datasets/statlog+))(german+credit+data), accessed on 13 February 2022). In this work, the characteristics of each client having credit are represented as a sequence. For instance, A40 represents if the client has a new car, and A71 represents if the client is unemployed;
2. **Bank Transactions**: this private dataset contains credit and debit card transactions in monetary units, grouped by the Classification of Individual Consumption by Purpose (COICOP), the international reference classification of household expenditure (COICOPs [[https://unstats.un.org/unsd/class/revisions/coicop_revision.asp](https://unstats.un.org/unsd/class/revisions/coicop_revision.asp)]([https://unstats.un.org/unsd/class/revisions/coicop_revision.asp](https://unstats.un.org/unsd/class/revisions/coicop_revision.asp)), accessed on 13 February 2022). Each bank user is represented by a sequence of COICOPs (C1 to C12). For instance, C1 represents food and non-alcoholic beverages, and C3 groups clothing and footwear;
3. **NYC and Tokyo Check-in**: this dataset contains 801131 check-ins in NYC and Tokyo collected from April 2012 to February 2013. Each check-in is associated with its timestamp, GPS coordinates, and venue categories (NYC and Tokyo Check-in: [[https://sites.google.com/site/yangdingqi/home/foursquare-dataset#h.p_ID_46](https://sites.google.com/site/yangdingqi/home/foursquare-dataset#h.p_ID_46)]([https://sites.google.com/site/yangdingqi/home/foursquare-dataset#h.p_ID_46](https://sites.google.com/site/yangdingqi/home/foursquare-dataset#h.p_ID_46)), accessed on 13 February 2022). To build the sequences, the venue categories (represented by letters) of the visited place were grouped for each user. For example, A represents a pet store, and B represents a beauty store.  
The sequence \(<\)[C1, C3, C3, C5]\(>\) is an example of the bank transaction dataset. Each \(C_{i}\) represents one of the twelve COICOPs. In the same way, other sequences' datasets have the same shape.  
Table 1 shows the main characteristics of the datasets. The number of sequences represents the number of rows in the database. The number of different items represents all the different objects in the database. The maximum and minimum sizes represent the maximum and the minimum number of items belonging to a sequence. Finally, the last column of Table 1 shows the average number of items in all sequences.  
It is worth noting that, before extracting the patterns, each dataset was encoded as sequences grouping the transaction ID per timestamp. In the following subsection, we present the different metrics performed over the aforementioned datasets. We highlight that all experiments were run on a PC Intel(R) Xeon(R) 2.10 GHz with 32 cores, RAM: 200 GB. All algorithms were implemented in Ubuntu 20.04.3 LTS using Python 3 using Jupyter, Pandas, Diffprivlib, Numpy, and Networkx.