  Privacy problems are lethal and getting more attention than any other issue
with the notion of the Internet of Things (IoT). Since IoT has many application
areas including smart home, smart grids, smart healthcare system, smart and
intelligent transportation and many more. Most of these applications are fueled
by the resource-constrained sensor network, such as Smart healthcare system is
powered by Wireless Body Area Network (WBAN) and Smart home and weather
monitoring systems are fueled by Wireless Sensor Networks (WSN). In the
mentioned application areas sensor node life is a very important aspect of
these technologies as it explicitly effects the network life and performance.
Data aggregation techniques are used to increase sensor node life by decreasing
communication overhead. However, when the data is aggregated at intermediate
nodes to reduce communication overhead, data privacy problems becomes more
vulnerable. Different Privacy-Preserving Data Aggregation (PPDA) techniques
have been proposed to ensure data privacy during data aggregation in
resource-constrained sensor nodes. We provide a review and comparative analysis
of the state of the art PPDA techniques in this paper. The comparative analysis
is based on Computation Cost, Communication overhead, Privacy Level, resistance
against malicious aggregator, sensor node life and energy consumption by the
sensor node. We have studied the most recent techniques and provide in-depth
analysis of the minute steps involved in these techniques. To the best of our
knowledge, this survey is the most recent and comprehensive study of PPDA
techniques.

Cryptography and Security