# An Effective Grouping Method for Privacy-Preserving Bike Sharing Data Publishing
## 2 Background and Related Works
### Privacy-Preserving Data Publishing  
To publish a dataset, there are trusted and untrusted model of data publishing [25]. In the untrusted publishing model, a data publisher might attempt to identify a user record and corresponding sensitive information from the dataset. Several anonymous communications and cryptographic solutions were proposed for collecting user data anonymously [26]. In the trusted privacy model, a data publisher remains trustworthy, and the record owners are reliable to provide their personal information for further processing. For example, a patient is ready to give her medical records to a hospital to obtain the needed medical service. Privacy-preserving data publishing is a trusted model of data publishing. The data publishing states that an organization is the data owner and the public is the data miner who wants to do significant research on the published dataset. An organization wants to publish its own microdata table \(T\) to the public. Microdata table \(T\) could be released directly to the public if it contains no sensitive information. Usually, a microdata table \(T\) contains sensitive information and the data owner cannot give \(T\) to the public in the raw format. When a dataset has been published to the other parties for data mining, privacy-preserving techniques are mandatory to reduce the possibilities of identifying the sensitive information about a person [27]. In privacy-preserving data publishing, one conjecture is that the data receiver could be an adversary. For example, a data mining research center is a responsible entity, but every staff in that organization will not be accountable as well. This hypothesis makes the privacy-preserving data publishing problems and solutions to be very distinct from the encryption and cryptographic methods, in which only authorized and reliable receivers are allowed for the private key toward accessing the cipher text [27; 28]. For privacy-preserving data publishing, the published microdata tables stand open to everyone. A significant challenge in privacy-preserving data publishing is to protect the privacy of a user withoutdisclosing their sensitive information. In addition, we have to ensure the data utility with the data privacy, namely the published dataset can be used for data mining and knowledge discovery. To preserve sensitive information, an anonymization techniques need to be applied to a published microdata table. The anonymization approach tries to protect the identity and the sensitive information of a user, assuming that sensitive data must be preserved for data analysis. At the time of data publishing, unique identifiers of a user must be removed from the datasets. Even after all unique identifiers being removed, [PERSON] [13] showed a real-life privacy threat to a former governor of the state of Massachusetts. In [PERSON]'s example, a user's name in a public voter list was linked to his record in a released medical dataset through the combination of zip code, date of birth, and sex. Each of these attributes does not uniquely identify a record owner. However, the aggregation of these attributes, which is \(QI\), usually find a unique or a small number of record holders. According to [PERSON] [13], 87% of the residents of the USA could be uniquely identified by using \(QI\) attributes. In the above example, the user is identified by linking his \(QI\) attributes. To perform such linking attacks [13; 18], the adversary needs two pieces of prior knowledge: (1) published dataset of a record holder and (2) the \(QI\) of the user. To limit linking attacks on the published dataset, we can provide an anonymization version of microdata table \(T^{\star}\)(_Quasi-identifier, Sensitive attributes_) by applying anonymization operation such as randomization [19], generalization [29] and perturbation [30]. In the modified microdata table \(T^{\star}\), \(QI\) is an anonymous version of the original \(QI\) of the primary dataset.