# An Effective Grouping Method for Privacy-Preserving Bike Sharing Data Publishing
###### Abstract  
Bike sharing programs are eco-friendly transportation systems that are widespread in smart city environments. In this paper, we study the problem of privacy-preserving bike sharing microdata publishing. Bike sharing systems collect visiting information along with user identity and make it public by removing the user identity. Even after excluding user identification, the published bike sharing dataset will not be protected against privacy disclosure risks. An adversary may arrange published datasets based on bike's visiting information to breach a user's privacy. In this paper, we propose a grouping based anonymization method to protect published bike sharing dataset from linking attacks. The proposed Grouping method ensures that the published bike sharing microdata will be protected from disclosure risks. Experimental results show that our approach can protect user privacy in the released datasets from disclosure risks and can keep more data utility compared with existing methods. bike sharing; identity disclosure; linking attacks; data publishing; privacy preservation +
Footnote †: journal:  
0000-0001-4507-22000/0000-0000-0000-0000-0000-0000-0000  
communication security, information security, and privacy violation, especially in health-care and personal data management [9; 10]. To implement the pervasive based system, the service provider should introduce a robust framework and reliable encrypted communication system for protecting user privacy and security [11; 12]. The data publisher has the microdata table in the form of _T_(_Identifier_, _Quasi-identifier_, _Sensitive attributes_) [13]. In the microdata Table \(T\), some attributes comprise the _Identifier_ that can identify a person, such as the social security number (SSN) or name. Some attributes determine _Quasi-identifier_ (_QI_) such as Birth Year, Gender, Start Station and End Station, and when these _Quasi-identifier_ attributes aggregate together it can identify a person. Some attributes express _Sensitive attributes_, which are unknown to the adversary and known as sensitive such as Start and End Station with Start and End Time. For data publishing, the identifier has been removed from the microdata table. In the last decade, numerous incidents of data privacy breaches occurred because of personal data sharing resulting in financial and reputation disasters for organizations [14; 15]. Table 1 exhibits an example of bike sharing transaction microdata, which remains open to the public [16]. User _ID_ and _name_ are never released during bike sharing data publishing. A user's sensitive information might still be leaked due to the presence of location and timing information [17]. Therefore, the requirement of anonymization implies breaking the relations among attribute values, so that a person will be indistinguishable in the released bike sharing transaction microdata table. Table 2 illustrates the _k-anonymized_[13] version of bike sharing transaction microdata Table 1.  
In this paper, we present a new approach called Grouping which preserves user privacy against a linking attack [13; 18]. In the anonymization, the Grouping method groups the dataset both vertically and horizontally. In the vertical grouping, location information and timing information have been grouped into the separate columns. In the horizontal grouping, the user travel records have been grouped in the buckets based on the trip duration. In a bucket, the attribute values randomly permute to break the correlation in between different columns. Hence, each _QI_ value will be linked with \(l\) distinct sensitive values and will reduce the confidence that the adversary will have when breaching personal privacy [19; 20]. Our proposed approach only generalizes the gender attribute column to ensure the user privacy. Therefore, it has less information loss and provides better data utility of the published datasets. A series of experiments on real-world dataset were conducted to support the effectiveness of the Grouping method.