a survey on the privacy preserving data aggregation in wireless sensor network

1. A survey on the privacy
preserving data aggregation in
wireless sensor networks
K.Sumanth
20951A04L1
Wcn

2. Introduction
• Wireless sensor network is made up of sensor nodes which collects information
about the environment and helps to understand and monitor the environment.
Now a day’s WSN assumes a key part in many real time applications like border
surveillance in military, seismic monitoring, weather monitoring, accident
reporting, health monitoring. But these sensor nodes have great concern for
efficient utilization of energy as they are equipped with low computational
power, less storage and less battery capacity. The major challenge is reducing
computation and communication cost. Communication cost depends on the
number of data moving in the network. This can be minimized by merging the
redundant data travelling over the network by using the technique called data
aggregation. The intermediate nremovinthe way to base station can aggregate
data either by removing the redundancy or by replacing the incoming data by
their aggregated representation which is based on the query being processed.
Thus the number of data packets transmitted are reduced which in turn reduce
the energy consumed by the WSN.

3. II SYSTEM MODEL
In sensor network, the sensor nodes can be organized and grouped as layers or
clusters. In layered networks the nodes are grouped such that all nodes in thesame
group can be reached from base station with same number of hops.

4. •In clustered networks, the sensor nodes
are grouped into clusters so that each
node inside a cluster comes under the
coverage of each other and controlled by a
cluster-head. Sensor nodes of same cluster
relay their detected information to their
group head. The group head advances the
got information to the Base Station (BS)
which is a door for different networks. Any
message can achieve the base station in at
most two bounces. The cluster head can
reduce the communication by acting as an
aggregator which will aggregate the data
sensed by all clustermembers before
forwarding it to BS. In most of the data
aggregation schemes like CDAMA, RCDA
nodes are organized as clusters.

5. III PRIVACY PRESERVING DATA AGGREGATION
PROTOCOLS
•The recent development in technology leads to the invention of miniature
sensors. Therefore, many applications have been proposed which are avaricious to
the network bandwidth, such as security surveillance and real-time target tracking.
Generally, a sensor node is equipped with a weak transceiver which does not
provide a large bandwidth.
•For this reason, the network design should strive to minimize the energy
consumption in propagating the data to the base-station. And most of the energy
in WSN is consumed by redundant information. Hence by eliminating data packets
carrying redundant information using data aggregation, energy can be saved.

6. Encrypted protocols:
•Encrypted protocols try to provide privacy by transforming the raw data into a
format which cannot be easily deciphered. But this requires pre distribution of
keys. And based on where encryption and decryption takes place, this encrypted
based protocols are again classified as hop by hop encryption based, privacy
homomorphism based , data slicing , secured multi party computation scheme. In
hop by hop encryption scheme, the data is decrypted before aggregation at every
intermediate aggregator node.
•Aggregation method is applied on raw data and encrypted again. It shows
distinguished performance for supporting data authentication, integrity and
confidentiality. But if the aggregator node is compromised, the sensitive data can
be easily revealed to attackers. So providing end-to-end data concealment to
protect privacy of data is the major challenge of this method.

7. Non-encrypted protocol
In data aggregation using watermarking method, watermark is embedded in the
original data. This prevents the adversaries from interpreting the actual
information being transferred from the sensor node to aggregator by embedding
watermarking in the original data. It does not require much calculation as in the
case of encrypted protocols. And it is also free from the need for generating and
distributing keys which consumes bandwidth and processor energy.

8. Concepts of Aggregation
•CDAMA (Concealed Data Aggregation for Multiple Applications) : This scheme is
proposed for cluster based wireless sensor network to provide secured data
aggregation using modified privacy homomorphism.
•RCDA (Recoverable Concealed Data Aggregation): This is a secured data
aggregation scheme for cluster based wireless sensor network. It ensures end to
end privacy of the data by using privacy homomorphism encryption.
•iPDA: This data slicing based aggregation scheme , splits the data into multiple
pieces and merges them at different levels of intermediate node.
•Hilbert-Curve Based Data Aggregation (HCBDA): This scheme proposes a novel
Hilbert-curve based data aggregation method that enforces data privacy and data
integrity for WSNs.

9. Integrity:
This ensures the consistency and trustworthiness of the data which can be
substantiated by the receiver. In CDAMA, The raw data of sensor nodes cannot be
separated from aggregated data. Hence integrity verification is limited .but RCDA
ensures the originality of data by attaching signature with the aggregated data
which can be verified by base station. But integrity and authenticity are not
ensured at aggregator level.
Data recovery:
It is the ability to recover individual sensor data from the aggregated value. In
CDAMA only application level aggregated data can be extracted by the base
station. Extracting individual sensor node is not supported. In RCDA the sensed
data from individual sensor nodes can be recovered. In iPDA only mixed up data is
available at the base station from the disjoint trees.

10. Conclusion:
In summary, CDAMA which support multi-application environment by
encapsulating the cipher text coming from different application into
one cipher text has high communication and computation cost and
does not provide support for integrity and different aggregation
function. RCDA, iPDA, HCBDA and CLWDA which ensure integrity is
limited to single application environment. We conclude that there is
always a tradeoff between different metrics in the design of various
aggregation schemes. The major challenge lies in customizing the
aggregations for various application domains with assured quality of
service.