## 4 Classification of PPDA Protocols
### Criteria of classification  
In a privacy-preserving data aggregation (PPDA) protocol, sensor data are partially exposed to neighboring trusted sensor nodes so that data aggregation can be achieved on the way to the sink node without revealing the actual data to the trusted sensor nodes or adversaries. There are many protocols available to achieve privacy-preserving data aggregation for WSNs. To the best of our knowledge, there is no comprehensive survey of the state of the art for privacy-preserving data aggregation for WSNs. The main challenge is to provide a new classification model for the PPDA protocols. First, the PPDA protocols can be classified based on a basis network topology, like routing protocols in the field of networking. In this type of classification, protocols are grouped into three classes; cluster, tree-structure and hybrid. But, PPDA protocols cannot be classified in this way because most PPDA protocols generally construct a common tree-structure as aggregation tree before transmitting data to the sink node. Secondly, a model can be used to classify protocols based on data collection methods. In this model, we can classify protocols into two groups: central collection and distributed collection. For example, query processing methods in spatial network databases can be grouped in this way. However, this classification model is not suitable for classifying the PPDA protocols because sensor nodes are resource-constraint in terms of processing speed, memory size and power supply. As a result, for distributed collection, a sensor node cannot store a large volume of sensor data for a long time. In a final classification model, we can classify the PPDA protocols based on a technique used to achieve privacy-preserving data aggregation for a WSN. The final classification model is more appropriate to classify PPDA protocols for WSNs because of the following three reasons. First, this model provides a broad range of groups available to classify protocols. The groups include perturbation, shuffling, hybrid and privacy homomorphism. Next, this model is appropriate to classify PPDA protocols for WSNs because the techniques used to achieve privacy-preserving data aggregation have their own peculiar features. For example, the privacy homomorphism technique is fast in terms of computation because it can execute arithmetic operations on cipher-text without decryption. As a result, the peculiar feature of a protocol can affect the issues of privacy-preserving data aggregation. At last, when the techniques are compared with each other in terms of privacy preserving and energy efficiency, this model leads us to explore which technique is the most suitable one in overall to achieve privacy-preserving data aggregation for WSNs.  
Figure 1 illustrates the classification of the existing PPDA protocols for WSNs. The PPDA protocols are broadly categorized into two categories: homogeneous protocols and heterogeneous protocols. They are categorized based on the type of nodes in the WSNs, particularly the type of data aggregating nodes (aggregators). The aggregators can either be special (more powerful) nodes or regular sensor nodes. Moreover, the protocols are further divided into four groups; perturbation, shuffling, privacy homomorphism and Hybrid. As we mentioned above, this division is based on the techniques used to achieve privacy-preserving data aggregation for WSNs.