# An Effective Grouping Method for Privacy-Preserving Bike Sharing Data Publishing
## 2 Background and Related Works
### Anonymization Techniques  
For data anonymization, there are several privacy models, such as the partition-based model [13; 31], the randomization-based model [19], and the differential privacy based model [32]. Among them, partition-based and randomization-based techniques are popular for privacy-preserving data publishing. In addition, recently differential privacy has received significant consideration for privacy-preserving data publishing. In the partitioning and randomization methods, the data values of \(QI\) (e.g., birth year, gender, start and end location) are generalized to construct a \(QI\) group. Therefore, an individual cannot be identified with their sensitive values in the group. Conversely, differential privacy answers the statistical queries based on the user request. In partition and randomization based anonymization techniques, there are some popular anonymization methods which have been proposed for privacy preserving in one-time data publishing. Among them, _k-anonymity_[13] and _l-diversity_[31] methods are more popular. _k-anonymity_[13] is the first proposed privacy model for data publishing. It requires that all records in a published dataset cannot be distinguished from at least _k-1_ other records. _k-anonymity_ does not consider sensitive attributes so that attackers may learn the relationship between sensitive data and individuals through a background knowledge attack. Background knowledge attack was proposed in [31]. Background knowledge attack means that an adversary could use background knowledge to discover sensitive information, such as general understanding or professional knowledge about the published dataset. To address the drawbacks of the _k-anonymity methodology_, the _l-_diversity privacy model was proposed [31]. A \(QI\) group is _l-_diverse if the probability that any tuple in this group remains associated with a sensitive value is at most \(1/l\). Both _k-anonymity_ and _l-_diversity use the generalization technique to anonymize the microdata. For the generalization, the data table loses an enormous amount of information, particularly for higher dimensional data [19]. Generalization breaks the correlation between attributes and it assumes that any possible combinations of attribute values are equally possible, i.e., Person (Figure 1) expresses the generalized value for the attribute values Female and Male. Generalization implies an anonymization technique to coarsen the values by mapping a value to an interval or a particular concept to a more general one [29]. It has been widely used for data anonymization. An advantage of generalization holds its faithfulness that a generalized value remains coarse but semantically consistent to its original value. For example, when age 24 is generalized to (20-30), we know that the age assumes a value in between 20 to 30 and it cannot assume a value of 32. Due to the faithfulness, when an adversary sees a set of records containing the values of a user ID, the adversary knows that the user's record remains in the set. Such information facilitates the disclosure risks. Therefore, generalization alone cannot prevent a dataset from a privacy attack [14]. The bike sharing companies published their datasets in every month, and each released dataset may contain multiple riding information of a single user. We consider that a registered user frequently rides a bike and, for every ride, the dataset will have her riding information. The bike sharing dataset contains the start and end locations of a riding path with the start and end time. A user future movement patterns can be predicted from the past locations [33]. In addition, some research works show that our actions are easily predictable by nature [34; 35]. When a dataset contains user information more than one time then, by arranging the \(QI\) values, an adversary may know the user's identity with the visiting locations, which may lead to a privacy breach [33].