## 2 Motivation and Background
### Privacy Preserving Data Collection Models  
Studies on privacy problem mostly concentrated on achieving collection and sharing of data under the required privacy constraints in order to make efficient knowledge-based decisions. Data collection refers to the conveying of user data from data owners to a central data collector. In some situations, data collector may share this data with other third parties. Privacy preserving data collection is modelled according to the trust level between data owner and data collector party. These models are categorized into two categories, _trusted data collection model_[4] and un-trusted data collection model [8]. In trusted data collection model, [4] as shown in Figure 1, data is collected from data owners by data collector party named as _data publisher_. Data publisher shares data with _data recipient_ who will actually use it for performing a required data analysis task. Here, data collection and data sharing areseparate operations. A generic example is the application where hospitals share medical records with medical research institutions. In this example, data owners are patients, data publishers are hospitals and data recipients are medical research institutions. Data publishers collect all the details of records of data owners \((R_{1},R_{2},\cdot\cdot\cdot R_{n})\) but they are required to share privacy preserved data with data recipients. Data owners do not trust data recipient parties; however, they are required to fully trust data publishers in completing privacy preserving operations. Also data owners have to be sure that their data is not maliciously or unintentionally used for illegal duties by staff of data publishers. An intrinsic assumption of this model is that data publisher does not know the details of analysis or data mining tasks which will be performed by data recipient. This may be due to situation that data publisher lacks technical expertise in the corresponding analysis methods or data publisher does not even know what type of analysis will be done on shared data in data recipient part. Privacy preserving data publishing methods are used to cover the requirements of this model. In un-trusted data collection model as shown in Figure 2, data owners send their records to data collector but they do not trust it. Data collection and data sharing do not occur separately as in the trusted model. Privacy preservation has to be done at the data owner side and privacy preserved data \((R^{{}^{\prime}}_{1},R^{{}^{\prime}}_{2},\cdot\cdot\cdot R^{{}^{\prime}}_{n})\) are collected by data collectors. Privacy preserved data mining methods use this model. In network applications that collect data from users, determination of analysis and mining tasks in the network design stage may limit the capability of this data collection system. Also, the requirements of analysis tasks may change with time. Therefore, in most of the time, privacy preserved data publishing methods are more useful in data collection applications. On the other side, these methods require a trusted data collector party or data publisher which collects centrally all the data. However, data owners may not fully trust them. Any person with malicious intent in the data collector site may have possibility to reach all the private data. Another possibility may be that due to inefficiency of security countermeasures at the data collector site or security problems during data collection operations, attackers can obtain private data. Data owners generally may want data to be privacy preserved before reaching these parties. Also, it is possible as in WSNs so that more than one sinks having different levels of privacy requirements may exist in the network. Therefore, a new model has to be devised so that privacy preserving data publishing methods can be applied for several un-trusted data collectors having  
Figure 1: Trusted data collection model. different privacy needs.