# An Effective Grouping Method for Privacy-Preserving Bike Sharing Data Publishing
## 2 Background and Related Works
### Problems in Bike Sharing Data Publishing  
Bike sharing data publishing presents a new challenge for privacy and utility for the published microdata table. Bike sharing data publishing is distinct from the traditional multiple time data publications, such as multiple view data publication [50; 51] and series data publication [38; 39; 52], since, in a bike sharing dataset, a single user's records exist multiple times in the dataset. An adversary may use only a single release to conduct the privacy attacks and carry on with every other version of the microdata table. In addition, traditional multiple time data publishing [38; 39; 52] uses Generalization [29] to anonymize the microdata table that decreases the data utility.  
A bike sharing microdata table consists of user's riding transaction records that are visited locations with timing information, and these are called points of interests (_POI_). A _POI_ can be any place for a person such as home, workplace, sports center or political party's office. In an attack against user privacy, the attacker applies user-specific travel information to breach the user's privacy [53]. The objective of this attack is to identify a user's house, workplace, and behavior. Analysis of bike sharing application data could cause a serious privacy breach of any user that might reconstruct her social networks, knowledge of her favorite visited places, her political and religious views.  
In the bike sharing published microdata table, an adversary may reveal a user identity and sensitive information by arranging user's records. For example, a public bike sharing user rides a bicycle to reach a bus stop to go to her workplace and come back home on weekdays. Therefore, the bike sharing transaction database will have the user's records for every time she rides a bicycle. An adversary may know her bus stop and arrange her records based on the bus stop and can know her Birth Year, Gender, Start Station and Start Time, which may lead to identifying the Stop Station and Stop Time. By using Birth Year, Gender, Start Station and Stop Station, the adversary may further search in the published microdata table and can arrange all of the user's available records for that particular route. The adversary could use these details of visiting information to initiate a physical or financial harassment to the user [54].  
We explain an example of how a bike sharing transaction dataset may breach a user's privacy. Table 1 presents the data segment from a bike sharing company [16]. By arranging the Birth Year, Gender, Start Station and End Station, we may find the person who frequently uses the bike sharing service. It is observed that a bike sharing transaction microdata table has multiple records of a single user. From Table 1, it can be recognized that a male user born in 1970 often rides a bike from E 47 St and Park Ave to W 49 St and 8 Ave during the morning session and W 49 St and 8 Ave to E 47 St and Park Ave during the afternoon session. By arranging a particular user's bike riding information, an adversary might visit those places in person to infer the actual user identity. At present, the adversary has the identity of the person with his _POI_. The adversary might be a thief, and from the summary of the above information, the adversary could steal from his house. Therefore, we can conclude that, for bike sharing datasets, an adversary could breach the user privacy based on the published bike sharing transaction microdata table.