Privacy and Integrity Preserving Secure Data

Aggregation in Wireless Sensor Networks

Vivaksha J. Jariwala

and Devesh C. Jinwala

C. K. Pithawalla College of Engineering and Technology, Surat, India

S. V. National Institute of Technology, Surat, India

Abstract. The Wireless Sensor Networks (WSNs) protocols commonly use in-

network processing to optimize the communication costs. In-network pro-

cessing involves processing of the sensed data on-the-fly during the course of

the communication to the base station. However, due to the fusion of the data

items sourced at different nodes into a single one, the security of the aggregated

data as well as that of the aggregating node, demands critical investigation.One

of the approaches to ensure secure data aggregation is to use encrypted sensor

data for processing, using homomorphic encryption. Our research here is aimed

to propose an approach that uses homomorphic encryption and appropriate data

integrity mechanisms to offer confidentiality, privacy and data integrityfor se-

cure data aggregation in wireless sensor networks.

1 Introduction

Wireless sensor network is used for numerous applications including military surveil-

lance, facility monitoring and environmental monitoring [1]. Wireless sensor net-

works (WSNs) often consist of a large number of inexpensive, low-powered sensing

devices with limited memory, computational and communication capabilities [1].

Wireless sensor networks have number of sensor nodes that are able to communicate

with external node or the base station of network. The sensors communicate between

themselves to form a communication network like single-hop network, multi-hop

network or a hierarchical network with several clusters and cluster heads. Sensor

nodes frequently sense the data, process it and then transmit to the base station. It is

inefficient for all the sensor nodes to transmit the data directly to the base station as

these sensor nodes are energy constrained. If all the sensor nodes transmit their data to

the base station, precious energy of the wireless sensor networks is wasted [2].

Generally, data sensed by neighbouring sensor nodes are always highly correlated

and redundant. In addition to that, the amount of data generated in large sensor net-

works is usually enormous for the base station to process. Hence, we need methods

for combining data into high quality information at the sensors or intermediate nodes

that can reduce the number of packets transmitted to the base station resulting in con-

servation of energy and bandwidth. However, primarily due to the severe constraints

and secondarily due to the inherent increased costs in the communications relative to

that in processing, the WSNs follow in-network processing, wherein the emphasis is

on on-the-fly processing of the data packets before being communicated eventually to

Jariwala V. and Jinwala D..

Privacy and Integrity Preserving Secure Data Aggregation in Wireless Sensor Networks.

DOI: 10.5220/0004980100490060

In Proceedings of the 11th International Workshop on Security in Information Systems (WOSIS-2014), pages 49-60

ISBN: 978-989-758-031-4

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

the base station [3]. In in-network processing of the data, each sensor node senses the

required measurements and sends the data value to another node up in the hierarchy

called the aggregator node [1]. The aggregator node collects measurements from

different sensor nodes using that it generates a single representational aggregated

value by applying an aggregate function. Subsequently, instead of sending all the

messages towards the base station, the aggregator transmits only one aggregated re-

sult towards the base station [4].

However, while having advantage in a manner described above, the data aggrega-

tion operation certainly gives rise to other consequences. Data aggregator nodes usu-

ally collect data from the sensor nodes, apply aggregation operations on it and even-

tually communicate the processed data to the base station. Obviously, in the presence

of the malicious nodes, neither the aggregated data nor the aggregation operation

remains trustworthy. Thus, it is essential to ensure that the data aggregation opera-

tional paradigm is inherently secure.Thus, protocols for WSNs should be designed to

prevent malicious inside nodes from damaging the whole network’s functionality or at

least constrain their impacts to a reasonable level.

Amongst various security attributes in WSNs, we focus in this discussion on the

most important attributes privacy, confidentiality and data integrity. For privacy and

confidentiality we investigated various algorithms proposed in [5] for WSNs. We

select algorithm EC-OU based on the analysis given in [5]. The approaches to provide

data integrity can be either cryptography-based or non-cryptography-based. Our focus

here is only on cryptographic approaches. As per our literature survey, we categorize

the techniques for supporting data integrity in Secure Data Aggregation into three

classes viz. Signature based, Hash function based and Message Authentication Code

(MAC) based [6]. The digital signature based approach yields non-repudiation proper-

ty, however, entails higher overhead as compared to other approaches. To counter the

overhead due to the digital signature, our focus here is on the message authentication

code based integrity support for secure data aggregation in WSNs.

2 Secure Data Aggregation

Data aggregation functions are improving the bandwidth and energy utilization, it also

affect negatively to other various performance metrics like delay, accuracy, fault-

tolerance and security. As almost all the applications of WSNs demand certain level

of security, it is not possible to sacrifice security of WSNs for data aggregation [7].

There is a strong conflict between security protocols and data aggregation protocols.

Security protocols require encryption and authentication of data by each sensor nodes

to provide security while data aggregation protocols prefer plain data to perform ag-

gregation functions on it, to save energy and bandwidth of WSNs. Moreover, data

aggregation also modifies data by applying various aggregation functions i.e. sum,

average, min, max on it. Due to these, it is a challenging task to provide source and

data authenticity for data aggregation. Therefore, data aggregation and security proto-

cols must be designed together so that data aggregation can be performed without

sacrificing security.

2.1 Privacy Homomorphism

The fundamental basis for data aggregations are cryptographic methods that provide

privacy homomorphism property. A privacy homomorphism (PH) is an encryption

transformation that allows direct computation on encrypted data [8]. With the help of

privacy homormophism, data do not need to be decrypted at the intermediate nodes.

Hence, they are transmitted to the base station in such a way that information con-

tained in the nodes is not visible or accessible to the intermediate nodes. Hence, pri-

vacy of the data is preserved while being transmitted towards the base station. A cryp-

tosystem that supports single operation addition or multiplication is known as partial

homomorphic encryption (PHE).

Let Q and R denote two rings, and + and ⊕ denote addition operations on the

rings. Let k denotes the key space. We denote an encryption transformation E: K X Q

R and the corresponding decryption transformation D : K X R Q. Given a, b є Q

and k, k

, k

є K,

a + b = Dk (Ek(a) ⊕Ek(b))

additively homomorphic with a single secret key and

a + b = Dk (k

) (Ek

(a) ⊕ Ek

(b))

additively homomorphic with multiple secret keys. We denote an asymmetric addi-

tively homomorphic encryption transformation as

a + b = Dp (Ep(a) ⊕Eq(b))

with (p,q) being a private, public key pair.

With the help of privacy homomorphism secure data aggregation can be achieved

by either using symmetric key cryptography or public key cryptography. Symmetric

homomorphic encryption requires use of identical key by encryption and decryption

while asymmetric homomorphic encryption requires use of public key-private key for

encryption and decryption.

2.2 Related Work

In [9], the authors propose secure and efficient scheme for data aggregation in WSNs.

The proposed approach uses Castelluccia-Mykletun-Tsudik [10] scheme for encryp-

tion. Castelluccia-Mykletun-Tsudik is a symmetric key cryptography based scheme

and suffers from the key management issues. The attempt in [11] also follows similar

approach of symmetric key cryptography. In [12], the authors propose a scheme for

SDA using elliptic curve cryptography that achieves only confidentiality without

integrity. None of the approaches discussed so far, support confidentiality as well as

message integrity that the authors in [13], attempt to do. In [14], authors propose ECC

based SDA scheme that provides confidentiality and integrity, but use EC-EG for

encryption. However, as per empirical evaluation in [5], energy consumption as well

as decryption time is more for this algorithm. In [15], authors propose scheme for

secure data aggregation that focuses only on confidentiality and privacy not on integ-

rity. In [16], authors propose scheme of secure data aggregation but that do not pre-

serve privacy of the sensed data. In [17], authors propose secure data aggregation

based on data slicing technique but without integrity support.

As mentioned before, we do not observe any research attempt in using such a

benchmarked privacy homormophic encryption algorithm[5] EC-OU in a tree topolo-

gy, to showcase truly securedata aggregation. Thus, we believe ours is the first and

unique attempt in integrating EC-OU in the tree topology for providing privacy and

confidentiality with MAC based integrity support for secure data aggregation in

WSNs.

3 Proposed Approach of Privacy Preserving Secure Data

Aggregation

3.1 Algorithms

In our scheme, we propose two algorithms, first one for the sensor node and the other

one for the base station. We discuss the algorithms with the help of their pseudo code

further. Each sensor node will execute SensorNodeAlgo( ) that takes plain text mes-

sage and private key as input to the algorithm. Next, each sensor node will compute

its public key by multiplying its private key to the base point of elliptic curve E. Sub-

sequently, each sensor node comprise two large prime number p & q and calculate n

as public key and calculate H=nG with point multiplication. Next, sensor node will

compute the cipher text and transmit it to its parent node.

ciphertext C = mG + rH

The parent node receives cipher text from the child nodes, performs the summation

of all the received cipher texts and finally transmits the aggregated cipher text to the

base station.The pseudo code for the proposed algorithm is shown below:

_________________________________________________________

Algorithm 1:SensorNodeAlgo()

// Maps its reading m

on the elliptic curve E

// Elliptic Curve Parameters E = (q, a, b, G, p, h)

// Each sensor node will computes following

1. Public Key: n = p

q, G, H, Q=pG

2. Encryption: plaintext m < 2

k-1

,r •

Ciphertext C = mG + rH

3. if sensor is a parent (Aggregator Node)

c = • c

// combines all cipher texts into one cipher text

end if

_________________________________________________________

The base station algorithm takes cipher text as input to the algorithm. Base station

then decrypts the incoming packets and calculates plain text that is the summation of

all the messages from all the sensor nodes.

_________________________________________________________

Algorithm 2: BaseStationAlgo( )

// Maps its reading c

from the elliptic curve E

// Elliptic Curve Parameters E = (q, a, b, G, p, h)

// Base station will computes following

1. Public Key:n = p

q, G, H, Q=pG

2. Private Key: p

3. Computem

•

•

(mod p)where, •p(x, y) = -





(mod p

)

4. Base station can get m=•m

_________________________________________________________

3.2 Example

Example of secure data aggregation of our propose approach is there in figure 1. In

this, nodes 3 and 4, first encrypt messages 3 and 5, respectively using EC-OU privacy

homormophic encryption function and send resultant cipher texts to node 1. Node 1

receives cipher text from nodes 3 and 4, aggregates them using the addition operation

(SUM), and forwards it to node 0 that acts as a base station.

Fig. 1. Example of Privacy Preserving Secure Data Aggregation

In the same manner, nodes 5 and 6 encrypt messages 3 and 4, respectively using

EC-OU privacy homomrophic encryption function and forward them to node 2. Node

2 receives both the encrypted data, perform aggregation function, and send the result-

ant aggregated result to node 0. After receiving both the aggregated result from node

1 and 2, node 0 performs the aggregation operation. After aggregation, node 0 applies

EC-OU decryption function to decrypt the aggregated result. As shown in fig. 1, de-

crypted output is message 15 that is the summation of messages 3,5,3,4 of nodes 3, 4,

5, and 6 respectively. Hence, data are transmitted to the base station in such a way

that their privacy is preserved.

4 Proposed Approach of Integrity Preserving Secure Data

Aggregation

In this section, we discuss MAC based hop-by-hop integrity support for secure data

aggregation in WSNs. In hop-by-hop integrity, if malicious adversary insert any false

data in the networks that will be detected immediately at next hop. Therefore, that

malicious data do not travel towards the base station. In contrast to that, in end-to-end

integrity, false data are detected only at the end i.e. at the base station, wasting energy

and lifetime of sensor networks. Thus, our proposed approach of hop-by-hop integrity

in secure data aggregation save bandwidth of the sensor networks and increase the

lifetime as well as security of the sensor nodes.

4.1 Algorithms

In our proposed approach, algorithm 1 is to be implemented on the leaf node. Each

sensor node computes M

on the outgoing message m

= MAC(m

)

Parent of sensor node receives M

and m

and computes M

on received m

. If re-

ceived M

is the same as computed M

, parents accept the message and apply aggrega-

tion on the message. After aggregation, parents compute MAC on aggregated mes-

sage and send it further.

_________________________________________________________

Algorithm 1:Leaf Node()

// Each leaf node will computes following

MAC Generation: Each sensor computes

= MAC(m

)

Append this M

to message and

send it to parent node

_________________________________________________________

Algorithm 2 is to be implemented on the aggregator node and the base station. Ag-

gregator node or base station will receive aggregated message and MAC of sensor

nodes. Aggregator node or base station again compute MAC on the received message

and verify that weather received MAC is same as computed MAC or not. If it is not

same, aggregator node simply discard the message. Thus, our approach ensures hop-

by-hop integrity through MAC.

_________________________________________________________

Algorithm 2: Aggregator Node and Base Station()

// Aggregator Node and Base station will computes following

Verification of MAC:

Aggregator or Base station receive M

and m

Compute M

= MAC(m

)

Verify Computed M

= received M

Base station can get m = • m

_________________________________________________________

4.2 An Illustration

In our approach of hop-by-hop MAC based integrity, each leaf node shares secret key

with parent and each leaf node generates MAC using SHA-1on the outgoing message

with the help of the shared key. Similarly, after receiving the message and computed

MAC from child, aggregator node or Base station will again compute MAC with the

key shared with child and verify the integrity of the message. For example, in our

approach, node 3 generates MAC on data of node 3 so M3 (MAC 3) is generated that

is received by node 1. Similarly node 4 generate MAC on data of node 4 so M4

(MAC 4) is generated that is also received by node 1. Now node 1 have M3 (MAC 3)

and M4 (MAC 4), so node 1 verify M3 and M4. If it is verified, then only node 1 will

accept message from node 3 and 4 and apply aggregation function on it and generate

m1. Otherwise, node 1 will not accept the messages from node 3 and node 4 and

simply discard the messages. If node 1 has accepted messages from node 3 and node

4 and generated aggregated message, node 1 apply MAC on aggregated message and

generate M1 (MAC 1). Same way node 0 (Base station) accept messages from node 1

and node 2 only if M1 (MAC 1) and M2 (MAC 2) is verified. In fig. 2, this scenario is

shown.

Fig. 2. Example of Integrity Preserving Secure Data Aggregation.

5 Combined Approach of Privacy and Integrity Preserving

Secure Data Aggregation

In this section, we discuss integration of MAC based Hop-by-Hop integrity with pri-

vacy and confidentiality.

5.1 Pseudo Code

In our proposed approach of secure data aggregation algorithm 1 is to be implemented

on the sensor node. Each sensor node generates cipher text c

of the message.

=Enc(m

)

In this, each sensor node computes M

on the outgoing message C

= MAC(C

)

Parent of sensor node receives M

and C

and parent computes M

on received C

. If

received C

is same as computed C

, parent accepts the message and applies aggrega-

tion on message. After aggregation, parent compute MAC on aggregated message and

send it further.

_______________________________________________________________

Algorithm 1: SensorNodeAlgo()

// Maps its reading m

on the elliptic curve E

// Elliptic Curve Parameters E = (q, a, b, G, p, h)

// Each sensor node will computes following

Public Key: n = p

q, G, H, Q=pG

Private Key: p

Encryption: plaintext m

< 2

k-1

,r•

Cipher text C

= m

G + rH

MAC Generation: Each sensor computes M

= MAC(C

)

if sensor is a parent (Aggregator Node)

Compute M

= MAC(C

)

Verify Computed M

= received M

if verified go to next step

C = • c

//combines all cipher texts into one cipher text and

send it to parent node

End if

_______________________________________________________________

Algorithm 2 is to be implemented on the base station. Base station will receive aggre-

gated cipher text and MACof the sensor nodes. Base station again compute MAC on

the received message and verify that weather received MAC is same as computed

MAC or not. If it is same then apply decryption function on aggregated cipher text

and get the original message that is summation of all the plain text messages. Thus,

our approach ensures confidentiality and privacy through privacy homomorphic en-

cryption and in addition to that, it ensures hop-by-hop integrity through MAC.

_____________________________________________________________

Algorithm 2: BaseStationAlgo()

// Maps its reading c

from the elliptic curve E

// Elliptic Curve Parameters E = (q, a, b, G, p, h)

// Base station will computes following

1. Public Key:n = p

q, G, H, Q=pG

2. Private Key:p

3. Verification of MAC: Compute M

= MAC(C

)

Verify Computed M

= received M

if verified go to next step

4. Compute m =





(mod p)

•p(x, y) = -





(mod p

)

5. Base station can get m = • m

_____________________________________________________________

5.2 An Illustration

Example of our framework of secure data aggregation is there in figure 3. The nota-

tions used are as follows:

Msg = Message of sensor node

MAC

= MAC computed using SHA-1by i

node

= Cipher text generated by i

node

Fig. 3. Example of Privacy and Integrity Preserving Secure Data Aggregation.

6 Implementation Methodology and Results

6.1 Platform and Tools Used

We devise an application testECOUM for ECC based privacy homomorphic encryp-

tion algorithm in the TinyOS 1.x operating environment [18] using TinyECC [19]

library, with nesC [20] as the language for implementation. Our evaluation is based

on a two-step approach: a) we use TOSSIM [21] as the WSN simulator; using that we

also obtain the estimates of the storage requirements of the respective implementa-

tion, b) energy consumption in Joules using the Avrora emulator [22].

6.2 Results

Table 1. % Increase Compared to without SDA.

Framework ROM in

bytes

RAM in

bytes

Energy Consump-

tion in μJoules

Confidentiality and privacy

preserving SDA

4.45% 7.59% 0.002%

Confidentiality, Privacy and

Integrity preserving SDA

20.06% 36.45% 0.008%

Table 1 shows the summary of our results. It shows percentage increase in results

compared to that of without secure data aggregation. Confidentiality and privacy

preserving approach of SDA requires 4.45 % more ROM and 7.59 % more RAM

compared to that of without SDA. Our MAC based hop-by-hop approach requires

20.06 % more ROM and 36.45 % more RAM.

Figure 4 shows ROM requirements of data aggregation with various options. (A)

without SDA (only data aggregation) (B) data aggregation that supports privacy and

confidentiality (C) data aggregation that supports MAC based hop-by-hop integrity in

addition of privacy and confidentiality.

Fig. 4. ROM Requirements.

Fig. 5. RAM Requirements.

Fig. 6. Energy Consumptions.

Figure 5 shows RAM requirements and figure 6 shows energy consumptions. Figures

clearly shows that if we add integrity in addition of privacy and confidentiality, then

resource consumption and energy consumption increases. This is because the compu-

tation involves in computing MAC on each message but that is at the advantage of

providing support for all the required security attributes.

7 Conclusion

In this research exercise we propose privacy, confidentiality and integrity preserving

secure data aggregation in Wireless Sensor Networks. From our experimentations, it

is observed that all the necessary security attributes for secure data aggregation are

preserved within life time of the resource constrained environment of WSNs. Our

approach requires less than 21% ROM, less than 37% RAM and less than 1% increase

in energy consumption. Our results show that our approach of integrity, privacy and

confidentiality preserving secure data aggregation is feasible in WSNs.

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