Two Secure Privacy-Preserving Data Aggregation Schemes for IoT

Nowadays, IoT technology is used in various application domains, including the healthcare, where sensors and IoT enabled medical devices exchange data without human interaction to securely transmit collected sensitive healthcare data towards healthcare professionals to be reviewed and take proper actions if needed. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range. In healthcare applications, many miniaturized devices are exploited for healthcare data collection and transmission. Thus, there is a need for secure data aggregation while preserving the data integrity and privacy of the patient. For that, the security, privacy, and aggregation of health data are very important aspects to be considered. This paper proposes a novel secure data aggregation scheme called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based to verification and authorization phase to verify the legitimacy of the nodes that need to join the process of aggregation. EPPDA, also, uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The major advantage of homomorphic encryption is allowing complex mathematical operations to be performed on encrypted data without knowing the contents of the original plain data. The proposed system is developed using MySignals HW V2 platform. Security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EPPDA: An Efficient and Privacy‐Preserving Data Aggregation Scheme with Authentication and Authorization for IoT‐Based Healthcare Applications

Almalki

Soufiene

2021

“…In [26], the authors have proposed two schemes based on slicing for secure data aggregation. In Scheme I, for the network with n nodes, each data is sliced into n pieces and confusion is added by mixing the slices of n nodes.…”

Section: Related Workmentioning

confidence: 99%

Discrete Integrity Assuring Slice‐Based Secured Data Aggregation Scheme for Wireless Sensor Network (DIA‐SSDAS)

Vinodha

Anita

2021

In a wireless sensor network, data privacy with a minimum network bandwidth usage is addressed using homomorphic-based data aggregation schemes. Most of the schemes which ensure the end-to-end privacy provide collective integrity verification of aggregated data at the receiver end. The presence of corrupted values affects the integrity of the aggregated data and results in the rejection of the whole data by the base station (BS) thereby leading to the wastage of bandwidth and other resources of energy constraint wireless sensor network. In this paper, we propose a secured data aggregation scheme by slicing the data generated by each sensor node deployed in layered topology and enabling en route aggregation. Novel encoding of data and hash slices based on child order is proposed to enable concatenation-based additive aggregation and smooth extraction of slices from the aggregate by the BS. Elliptic curve-based homomorphic encryption is adopted to ensure end-to-end confidentiality. To the best of our knowledge, the proposed scheme is the first which facilitates the BS to perform node-wise integrity verification, filter out only the corrupted portion, and implement dynamic query over the received data. Communication- and computation-based performance analysis shows the efficiency of the proposed scheme for varied network sizes. The scheme can resist eavesdropping attack, node compromising attack, replay attack, malleability attack, selective dropping attack, and collusion attack.

“…Privacy Preserving (PP) has attracted much attention in many fields, such as smart grid [14], Internet of ings [15,16], edge computing [17], social network [18] and other application scenarios [19][20][21]. PP can also protect the privacy of sensing data when DA is adopted in a WSN, and some interesting schemes have been proposed in recent years [22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Query Privacy Preserving for Data Aggregation in Wireless Sensor Networks

Liu

Zhang

et al. 2020

Self Cite

Wireless Sensor Networks (WSNs) are increasingly involved in many applications. However, communication overhead and energy efficiency of sensor nodes are the major concerns in WSNs. In addition, the broadcast communication mode of WSNs makes the network vulnerable to privacy disclosure when the sensor nodes are subject to malicious behaviours. Based on the abovementioned issues, we present a Queries Privacy Preserving mechanism for Data Aggregation (QPPDA) which may reduce energy consumption by allowing multiple queries to be aggregated into a single packet and preserve data privacy effectively by employing a privacy homomorphic encryption scheme. The performance evaluations obtained from the theoretical analysis and the experimental simulation show that our mechanism can reduce the communication overhead of the network and protect the private data from being compromised.