Secure Data Aggregation in Wireless Sensor Networks Using Privacy Homomorphism

In‐network data aggregationin wireless sensor networks (WSNs) is a technique aimed at reducing the communication overhead—sensed data are combined into partial results at intermediate nodes during message routing. However, in the above technique, some sensor nodes need to send their individual sensed values to an aggregator node, empowered with the capability to decrypt the received data to perform a partial aggregation. This scenario raises privacy concerns in applications like personal health care and the military surveillance. A few other solutions exist where the data are not disclosed to the aggregator (e.g., using privacy homomorphism (PH)), but these solutions are not robust to node or communication failure.The contributions of this paper are two‐fold: first, we design a private data aggregation protocol that does not leak individual sensed values during the data aggregation process. In particular, neither the base station (BS) nor the other nodes are able to compromise the privacy of an individual node's sensed value. Second, the proposed protocol is robust to data‐loss; if there is a node‐failure or communication failure, the protocol is still able to compute the aggregate and to report to the base station the number of nodes that participated in the aggregation. To the best of our knowledge, our scheme is the first one that efficiently addresses the above issues all at once. Copyright © 2009 John Wiley & Sons, Ltd.

The Wireless Sensor Networks (WSNs) after their emergence have triggered many active research areas; not because of low-scale hardware design but also due to a wide spectrum of its potential applications being in day-to-day areas of our life. However, because of the ubiquitous and pervasive computing paradigm that the WSNs foster and because of the resource constrained characteristic, security & privacy issues therein assume critical investigation. Amongst many potential aspects of investigation, one has been the design of a secure routing protocol in which a value sensed to be delivered to an appropriate receiver securely and efficiently with least amount of energy consumption. We observe that LEACH (Low Energy Adaptive Clustering Hierarchy) is a popular clustering based protocol used in WSNs. However, the same lacks any security support. Amongst numerous improvements of LEACH suggested to incorporate security & privacy, we observe that none, to the best of our knowledge offers end-to-end privacy using the homomorphic encryption schemes. We propose here an improvement of LEACH viz. E-LEACH (end-to-end secure LEACH) which uses homomorphic encryption to provide secure data aggregation in Wireless Sensor Networks. Due to the algebraic characteristics in homomorphic encryption, data can be algebraically aggregated without decryption and hence require least amount of energy, while ensuring privacy.

The wireless communication nature of remotely deployed sensor nodes make the attacks more easily to be happened in wireless sensor networks (WSNs). But traditional security algorithms are infeasible in WSNs due to the limited computing, communication power, storage, band width and energy of sensor nodes. So energy efficient secure data aggregation schemes are necessary in resource constrained WSNs. Concealed Data Aggregation (CDA) based on privacy homomorphism (PH) gives a critical solution for energy efficient secure data aggregation in WSNs. PH based algorithms allow aggregation to be happened on cipher texts. Thus, it eliminates the power consuming decryption operations at the aggregator node for the data aggregation and further encryption for the secure transmission of aggregated data. It also avoids the aggregator node from the burden of keeping the secret key information and thereby it achieves energy efficiency and reduces the frequency of node compromise attacks in aggregator nodes. Among the CDA techniques, asymmetric PH based CDA techniques are exploring due to their combination with elliptic curve cryptography having reduced key size. We present an overview of asymmetric concealed data aggregation techniques that achieve both end to end data confidentiality and non delayed data aggregation.

Wireless sensor networks (WSNs) are increasingly used in many applications, such as volcano and fire monitoring, urban sensing, and perimeter surveillance. In a large WSN, in-network data aggregation (i.e., combining partial results at intermediate nodes during message routing) significantly reduces the amount of communication overhead and energy consumption. The research community proposed a loss-resilient aggregation framework called synopsis diffusion, which uses duplicate-insensitive algorithms on top of multipath routing schemes to accurately compute aggregates (e.g., predicate count or sum). However, this aggregation framework does not address the problem of false subaggregate values contributed by compromised nodes. This attack may cause large errors in the aggregate computed at the base station, which is the root node in the aggregation hierarchy. In this paper, we make the synopsis diffusion approach secure against the above attack launched by compromised nodes. In particular, we present an algorithm to enable the base station to securely compute predicate count or sum even in the presence of such an attack. Our attack-resilient computation algorithm computes the true aggregate by filtering out the contributions of compromised nodes in the aggregation hierarchy. Extensive analysis and simulation study show that our algorithm outperforms other existing approaches.

Data aggregation is an important method to reduce the energy consumption in wireless sensor networks (WSNs), however, performing data aggregation while preserving data confidentiality and integrity is mounting a challenge. The existing solutions either have large communication and computation overheads or produce inaccurate results. This paper proposes a novel secure data aggregation scheme based on homomorphic encryption in WSNs. The scheme adopts a symmetric-key homomorphic encryption to protect data privacy and combines it with homomorphic signature to check the aggregation data integrity. In addition, during the decryption of aggregated data, the base station is able to classify the encrypted and aggregated data based on the encryption keys. Simulation results and performance analysis show that our mechanism requires less communication and computation overheads than previously known methods. It can effectively preserve data privacy, check data integrity, and achieve high data transmission efficiency. Also, it performs accurate data aggregation rate while consuming less energy to prolong network lifetime.

Wireless Sensor Networks (WSNs) consist of a number of limited energy sensor nodes deployed randomly over an area. The paper proposes a fuzzy knowledge based secure tree-based data aggregation mechanism for WSNs called the Fuzzy knowledge based Data Aggregation Scheme (FDAS). In the proposed FDAS mechanism, a combination of fuzzy rules is applied to predict the node status of each node in the aggregation tree. The faulty nodes are then isolated from the process of data aggregation. The proposed FDAS mechanism also ensures the security of the network by the application of the privacy homomorphic cryptography technique. It has been found to give better performance characteristics than the other existing algorithms as validated through the results obtained from simulation.

Data aggregation has been one of the most widely researched topics for Wireless Sensor Network (WSN) as it helps in increasing the effective life time of the network and reduces the packet transmission overhead. Besides other challenges, security is one of the serious challenges during data aggregation. Numerous solutions on security for data aggregation for WSN are based on homomorphic encryption technique or Elliptic Curve Cryptography (ECC) based solutions and thus are vulnerable to attacks by quantum machines. This paper presents a post-quantum security solution using lattice cryptography. The proposed solution is applying Learning with Errors over Rings (R-LWE) for encryption of data in the data aggregation process at the gateway module of WSN. Security analysis shows that the proposed scheme provides confidentiality, integrity and authenticity during communication. Performance analysis shows that the proposed scheme is lightweight and shows better performance compared to Elliptic Curve Elgamal (ECEG) cryptography based scheme and symmetric homomorphic based scheme.

Nodes in a wireless sensor network are normally constrained by hardware and environmental conditions and face challenges of reduced computing capabilities and system security vulnerabilities. This fact calls for special requirements for network protocol design, security assessment models, and energy-efficient algorithms. Data aggregation is an effective energy conservation technique, which removes redundant information from the data aggregated from neighbor sensor nodes. How to further improve the effectiveness of data aggregation plays an important role in improving data collection accuracy and reducing the overall network energy consumption. Unfortunately, sensor nodes are normally deployed in an open environment and thus are subject to various attacks conducted by adversaries. Consequently, data aggregation brings new challenges to wireless sensor network security. In this article, we propose a novel secure data aggregation solution based on autoregressive integrated moving average model, a time series analysis technique, to prevent private data from being learned by adversaries. We leverage the autoregressive integrated moving average model to predict the data volume in sensor nodes, and update and synchronize the model as needed. The experimental results demonstrate that our model provides accurate predictions and that, compared with competing methods, our solution achieves better security, lower computation and communication costs, and better flexibility.

Data aggregation processes aim to reduce the amount of exchanged data in wireless sensor networks and consequently minimize the packet overhead and optimize energy efficiency. Securing the data aggregation process is a real challenge since the aggregation nodes must access the relayed data to apply the aggregation functions. The data aggregation security problem has been widely addressed in classical homogeneous wireless sensor networks, however, most of the proposed security protocols cannot guarantee a high level of security since the sensor node resources are limited. Heterogeneous wireless sensor networks have recently emerged as a new wireless sensor network category which expands the sensor nodes’ resources and capabilities. These new kinds of WSNs have opened new research opportunities where security represents a most attractive area. Indeed, robust and high security level algorithms can be used to secure the data aggregation at the heterogeneous aggregation nodes which is impossible in classical homogeneous WSNs. Contrary to the homogeneous sensor networks, the data aggregation security problem is still not sufficiently covered and the proposed data aggregation security protocols are numberless. To address this recent research area, this paper describes the data aggregation security problem in heterogeneous wireless sensor networks and surveys a few proposed security protocols. A classification and evaluation of the existing protocols is also introduced based on the adopted data aggregation security approach.

Recent enhancement in wireless sensor networks (WSNs) have employed to enormous applications in real time fields which includes environment monitoring, health monitoring, military surveillance and target tracking. There is large consumption of energy when there is communication between the nodes. Data aggregation techniques can efficiently help to reduce the consumption of energy by eliminating unnecessary data travelling back to the sink. There are several security issues which include data integrity, data confidentiality, availability, and freshness in data aggregation that become critical when WSN is deployed in a hostile environment where sensors fall prone to node failures and compromised by adversary. Thus various security issues in data aggregation will be discussed. After that, the existing secure data aggregation schemes will be surveyed which are classified based on the number of aggregator nodes.

We propose two strategies to reduce energy consumption for secure data aggregation in wireless sensor networks. While the purpose of data aggregation is to reduce energy consumption, secure data aggregation introduces an overhead making the total energy consumption comparable to naive secure non-aggregation. Another aspect is that the secure data aggregation has higher node congestion than non-aggregation schemes for networks of sizes up to about 100 nodes. Considering a secure data aggregation protocol, our strategies are to reduce the height of the aggregation tree and limit its node degree. The former results in fewer verification messages and a lower total communication load, and the latter reduces node congestion. The most heavily burdened node sends a third of the messages required in the original protocol, which for small networks is lower than with non-aggregation, and the total communication load is lowered compared to non-aggregation.

We discuss the technology detail of data aggregation in wireless sensor networks (WSNs) in this paper, which consists of three parts. First, we apply the classical K-average clustering algorithm to the phase of cluster division which is the first step of the aggregate tree. Second, the cluster head node and the cluster group nodes are constructed based on the entropy formula H(S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> |x)<;F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</sub> (c) and pseudo-random function. Finally, we study the secure data fusion method among the nodes.

In a Wireless Sensor Network (WSN), specifically data aggregation reduces the amount of communication and energy utilization.Newly, the research centre has proposed a strong aggregation framework called synopsis diffusion which combines multipath routing schemes with duplicate-insensitive algorithms to perfectly compute aggregates (e.g., predicate Count, Sum) unkindness of message losing results from node and communication failures.But this aggregation framework does not solve the problems which are appearing at base station side.These problems may occur due to the irrespective of the network size, the per node communication over-head.In this paper, I make the synopsis diffusion approach secure against attacks in which compromised nodes put in false sub aggregate values.In particular, now present a novel lightweight verification algorithm by which the base station can determine if the computed aggregate (predicate Count or Sum) includes any false input.In this paper, I study the compromised node and denial-of-service is the two key attacks in wireless sensor networks.We disagree that multipath routing approaches are highly helpless to such attacks.So, for these attacks we develop the mechanisms that generate randomized multipath routes.In this designing, the routes are taken by the shares of dissimilar packets transform over time.So, we analytically examine the security and energy performance of proposed schemes.

This paper deals with information discovery and aggregation in large scale wireless sensor networks applied for mission-critical applications like military reconnaissance. To support query processing based on the gathered information, an efficient and reliable information discovery mechanism is proposed for sensor networks. We extend the basic Comb-Needle Discovery Support Model [3] by including Cluster-based data aggregation mechanism, which helps minimize the communication cost. Clusterbased approach groups the sensor nodes in the sensor network. Each node of a group will send information to its Cluster Head, which then aggregates and forwards the information to the base station (Sink). We compare the performance of proposed Cluster-based Comb-Needle Model with the basic Comb-Needle Model using the parameters, namely, energy consumption and communication cost. The experimental results through Avrora Simulator on TinyOS Platform reveal that Clusterbased Comb-Needle Model for Data Aggregation helps a sensor network in conserving its energy, and thereby extending its life time.

Data aggregation is an essential operation in many applications of Wireless Sensor Networks (WSNs). For energy constrained and delay sensitive WSNs, how to prolong the lifetime and reduce the latency of the network are two critical problems. We deal with these two problems together in data aggregation by proposing a Load Balanced and Delay Efficient Algorithm (LBDEA), which consists of two phases. First, construct a load-balanced data aggregation tree based on Semi-Matching algorithm in the graph theory. Second, seek a collision-free scheduling based on greedy strategy for the transmission links in the aggregation tree. Through balancing the load of the sensor nodes, the lifetime of the network is optimized. By avoiding collisions and retransmissions of data delivery, the total latency is decreased. Simulations show that LBDEA not only improves the lifetime but also cuts down the delay for data aggregation in WSNs, compared with other related algorithms.