## 1 Introduction  
Recently, wireless sensor networks (WSNs) [1-5] have been regarded as not only one of the eight technologies that could save the world [6], along with nuclear waste neutralizers, but also one of the ten emerging technologies that will change the world [7]. A WSN consists of a large number of spatially distributed autonomous resource-constrained tiny sensor devices [8] which are used to cooperatively monitor physical or environmental conditions, such as heat, temperature, sound, vibration, pressure, motion or pollutants, at different locations. WSNs have some unique features, for instance, limited power, ability to withstand harsh environmental conditions, ability to cope with node failures, mobility of nodes, dynamic network topology, communication failures, heterogeneity of nodes, large scale of deployment and unattended operation. Although sensor nodes forming WSNs are resource-constrained, _i.e._, have limited power supplies, slow processors and less memory, they are widely used in many civilian application areas, including environment and habitat monitoring, healthcare applications, home automation, traffic control and in military applications such as battlefield surveillance. Because data from sensor nodes are correlated in terms of time and space, transmitting only the required and partially processed data is more meaningful than sending a large amount of raw data. In general, sending raw data wastes energy because duplicated messages are sent to the same node (implosion) and neighboring nodes receive duplicate messages if two nodes share the same observing region (overlap). Thus, data aggregation, which combines data from multiple sensor nodes, has been actively researched in recent years. An extension of this approach is in-network aggregation [9-16] which aggregates data progressively as it is passed through a network. In-network data aggregation can reduce the data packet size, the number of data transmissions and the number of nodes involved in gathering data from a WSN. Another issue of WSNs is how to preserve sensitive measurements taken from everyday life where data privacy becomes an important aspect. In many scenarios, the confidentiality of transported data can be considered critical. For instance, data from sensors might measure patients' health information, such as heartbeat and blood pressure details. Also, as mentioned in [17], a future application might measure household details, such as power and water usage, computing average trends and making local recommendations. Since all data are transported wirelessly between sensor nodes, they are typically prone to interception and eavesdropping. Speaking broadly, there are two types of privacy concerns in WSNs: internal privacy and external privacy. The former is about maintaining the data privacy of a sensor node from other trusted participating sensor nodes of the WSN, whereas the latter means that the sensed data is protected from outsiders (adversaries). Data privacy can be simply defined as a process in which private data can be overheard and decrypted by adversaries or other trusted participating sensor nodes, but it can still provide a mechanism that prevents them from recovering sensitive information, _i.e._, control disclosure of any information about the data. To achieve data privacy, it is required to protect transmission trend of a node's private data from its neighboring nodes. This is because the neighboring nodes can always overhear the sum of the private data and a fixed unknown number, _i.e._, an encryption key. Therefore, in the field of WSNs, privacy-preserving data aggregation is becoming a hot issue in academia. To address this issue, some protocols have been proposed by researchers at various universities and institutions. In this paper, we provide a comprehensive summary and comparison of the existing privacy-preserving data aggregation protocols for WSNs. To the best of our knowledge, only the survey by [PERSON] _et al._[18] is related to privacy-preserving data aggregation protocols.