## 5 Related Work  
Studies on privacy problem mostly concentrated on achieving sharing of databases under the required privacy constraints in order to make efficient knowledge-based decisions. Generic name, \"Privacy Preserving Data Publishing\", is given to these efforts [4]. \(k\)-anonymity notion is introduced by [PERSON] and [PERSON] in [5]. It is shown that \(k\)-anonymization with minimum number of suppression is NP-hard [13]. Some optimal \(k\)-anonymization algorithms have been presented which may be feasible for small sized data sets [14, 15]. Greedy heuristics algorithms are proposed to find approximate solutions for large data sets [16, 17]. All these \(k\)-anonymity solutions solve the prevention of \"record linkage attack\" which is actually finding the owner of a record through quasi-identifier attributes. However, it is shown that without finding the exact owner of a record, if sensitive attribute exists in a record, it may be possible to identify sensitive attribute of an individual in some circumstances by an attack called \"attribute linkage attack\" [4]. This problem is also named as \"attribute disclosure\" [18]. In order to prevent attribute linkage and record linkage together, \(k\)-anonymity notion extended in some studies. [PERSON] _et al._ extended \(k\)-anonymity with a \(l\)-diversity notion that also prevents the identity disclosure when attackers have background knowledge [18]. Notion of \(p\)-sensitive is introduced so that \(p\) of \(k\)-anonymized records  
Figure 9: Performance comparison of topologies with different WSN area sizes. having identical quasi-identifier attribute values have to have distinct sensitive attribute values [19]. Generalization hierarchies are constructed for sensitive attributes and extended version of \(p\)-sensitive notion is adapted in [20]. An additional requirement, \(t\)-closeness, for \(l\)-diversity is defined in [21]. In this study, distribution of sensitive attributes in a record set having identical quasi-identifier attribute values are adjusted so that it is close to the distribution of that attribute in overall data set. Anonymity is considered as hiding the identities of sender or receiver of a communication in data and communication networks for many years. DC-Net and mix-net solutions are proposed for achieving sender or receiver anonymity [22, 23]. Especially, mix-net idea have been used in many practical Internet applications like web and e-mail [24, 25]. In ad-hoc networks, routing protocols for anonymous transmission of the data packets are designed. Studies about the anonymity problem in WSNs basically try to hide location or time information of the events. [PERSON] _et al._[26, 27] proposed anonymity solutions for providing high degree of privacy in a sensor network that gives location-based services. [PERSON] _et al._[28] proposed phantom routing method for hiding location information of originator sensor node in a sensor network. Threat model is based on an existence of only one movable adversary node in the environment. Location privacy protection of receiver in a WSN is provided by a routing protocol in [29]. Proposed routing protocol prevents the eavesdropper to identify the receiver by tracing the wireless packets. It randomizes the routing paths and injects fake packets in order to mislead eavesdroppers. [PERSON] _et al._[30] studied on providing anonymity of coordinate system, cluster and routing structures during the network setup of a WSN. Protection of location privacy is guaranteed by \(k\)-anonymity in location based services those are given on mobile networks [31]. Privacy studies of WSN community generally focuse on hiding sender or receiver entities from local or global eavesdroppers. However, in data collection applications, sender or receiver entities are known by all parties. Privacy threat models of data collection applications should concentrate on privacy of collected data against data collectors rather than hiding the communicated entities.