Verifiable, Reliable, and Privacy-Preserving Data Aggregation in Fog-Assisted Mobile Crowdsensing

Yan, Xingfu; Ng, Wing W. Y.; Zeng, Biao; Lin, Changlu; Liu, Yuxian; Lu, Lu

doi:10.1109/jiot.2021.3068490

Cited by 24 publications

(13 citation statements)

References 45 publications

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“…They have proposed a method, data membership group-based multiple-data aggregation, which first divides the smart grid meters into different groups so that these groups can generate an encrypted key for their data and then dynamic leave and join along with the meter replacement methods are used for the data aggregation. In the paper, Yan et al [6], for the fog nodes having untrusted servers, a data aggregation method for Fog-Assisted Mobile Crowd Sensing has been proposed for sharing the data among the users. The method preserves the privacy of the user's data and the aggregated data results.…”

Section: Figure 1 Data Aggregation In Wsnsmentioning

confidence: 99%

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Two phase secure data collection technique for wireless sensor networks

Thahniyath

Mishra

Moorthy

2022

IJEECS

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Wireless <span>sensor networks (WSNs) are the sensors that are dispersed in a different location that can sense the accumulated data in real-time and send it to the central location for the process of data aggregation. During the transfer of the information using the data nodes from the WSNs to the central location, there may be chances that the data node could be compromised by sensor failure or by an attack from a malicious user. To overcome this problem, we propose a two-phase secure data collection (TPSDC) technique for wireless sensor networks which provides confidentiality and integrity for the data nodes that are being sent to the central location and also during the data aggregation in the central location. Various existing methods have been proposed to secure the data when sent from the WSNs to the internet of things (IoT) devices but they lack to provide both confidentiality and integrity at the same time. Hence our model provides both integrity and confidentiality by providing security to the data nodes. Experimental results show that our model TPSDC performs better in terms of misclassification rate, detection rate, throughput, network lifetime analysis of the node, and communication overhead of the node when compared with the existing methods.</span>

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Section: Figure 1 Data Aggregation In Wsnsmentioning

confidence: 99%

“…Using these considerations, we can evaluate all the misclassified packets (nodes) which are trustable for the communication metric. This evaluation expression is given as (6).…”

Section: 𝑦 ̃𝑗 = 𝑁(𝑦 𝑗 ) = 𝑦 𝑗 + 𝛿 𝑗mentioning

confidence: 99%

Two phase secure data collection technique for wireless sensor networks

Thahniyath

Mishra

Moorthy

2022

IJEECS

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“…Decrypt all the ciphertext in RST t S i with Key i ; (7) if e end user cannot decrypt the ciphertext normally then (8) Completeness � false; return Completeness; (9) end if (10) Calculate the value of Ω i which is the total number of the queried locations in RST t S i ; (11) for j � 1 to Ω i do (12) if DPP t i,x j is not originally in RST t S i (DPP t i,x j is a Data-proof Packet corresponding to Loc t i,x j which is in QR I MWSN then (13) Completeness � false; return Completeness; (14) end if (15) Calculate the value of c t i,x j which is the total number of the sensed data items in DPP t i,x j ; ( 16) (33) if (n t i,x j is not included in DPP t i,x j in R t ) ‖ (no sensed data item in DPP t i,x j is encrypted with a sequence number) ‖ (the sequence numbers encrypted in DPP t i,x j are not sorted in ascending order from 1) ‖ (any sensed data item encrypted in DPP t i,x j is not originally encrypted with Loc t i,x j ) ‖ (E Key i μ t i,x j , Loc t i,x j is not originally included in DPP t i,x j ) then (34) Completeness � false; return Completeness; (35) end if (36) if n t i,x j � c t i,x j then (37) if c t i,x j ≠ μ t i,x j then (38) Completeness � false; return Completeness; (39) else…”

Section: Data Availabilitymentioning

confidence: 99%

“…e fog nodes may be captured by the nearby attackers or may suffer from the attacks arising from the cloud. In other words, the fog nodes may become untrusted [9,10] under such attacks. Meanwhile, the application servers in the cloud are facing many kinds of attacks, and some of the cloud servers may also not be trustworthy [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

STQ-SCS: An Efficient and Secure Scheme for Fine-Grained Spatial-Temporal Top- $k$ Query in Fog-Based Mobile Sensor-Cloud Systems

Min

Liang

et al. 2021

Security and Communication Networks

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With the emergence of the fog computing and the sensor-cloud computing paradigms, end users can retrieve the desired sensory data generated by any wireless sensor network (WSN) in a fog-based sensor-cloud system transparently. However, the fog nodes and the cloud servers may suffer from many kinds of attacks on the Internet and become semitrusted, which threatens the security of query processing in the system. In this paper, we investigated the problem of secure, fine-grained spatial-temporal Top- k query in fog-based mobile sensor-cloud systems (FMSCSs) and proposed a novel scheme named STQ-SCS to tackle the problem based on the virtual grid construction and the size-order encryption-binding techniques. STQ-SCS can preserve the privacy of the sensed data items and their scores and make end users verify the completeness of the query results of fine-grained spatial-temporal Top- k queries with a 100% successful rate even if the fog nodes and the cloud servers are not totally trustworthy. Besides the good security performance, simulation results indicate that STQ-SCS is also an efficient scheme that incurs a much lower communication cost than the state-of-the-art schemes on securing fine-grained spatial-temporal Top- k query in FMSCSs.

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“…Data aggregation is an essential prerequisite for data collection and information sharing in mobile crowdsensing networks. Data aggregation eliminates redundant information and extracts valuable information by processing local sensing data [4]. In smart grid applications, electricity consumption data is the basis for power companies to adjust power supply and demand control in real-time.…”

Section: Introductionmentioning

confidence: 99%

Grouping-Based Reliable Privacy Preservation for Blockchain-Assisted Data Aggregation in Mobile Crowdsensing

Wang

Yang

et al. 2022

Security and Communication Networks

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Privacy-preserving data aggregation is an important technology for mobile crowdsensing. Blockchain-assisted data aggregation enables the traceability of sensing data to improve the trustworthiness of data aggregation results. However, directly using blockchains for data aggregation may introduce the risk of privacy leakage because all nodes, including malicious nodes, can access the data on blockchains. In this paper, we propose a grouping-based reliable privacy-preserving data aggregation (RPPDA) method using private blockchains for mobile crowdsensing. First, the sensing nodes are divided into multiple groups, and each group maintains a private blockchain to store the data aggregation records, which avoids the leakage of the aggregated results and ensures the traceability of the sensory data. Then, a zero-sum noise-adding mechanism is utilized to not only preserve the private information during aggregation and ensure the correctness of the aggregated results but also improve the efficiency of privacy preservation. Furthermore, we theoretically prove the correctness, privacy, efficiency, and reliability of the proposed RPPDA algorithm. Real-world and simulated experiments demonstrate the effectiveness and advantages of the proposed RPPDA algorithm in terms of correctness, efficiency, and privacy.

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Verifiable, Reliable, and Privacy-Preserving Data Aggregation in Fog-Assisted Mobile Crowdsensing

Cited by 24 publications

References 45 publications

Two phase secure data collection technique for wireless sensor networks

Two phase secure data collection technique for wireless sensor networks

STQ-SCS: An Efficient and Secure Scheme for Fine-Grained Spatial-Temporal Top- $k$ Query in Fog-Based Mobile Sensor-Cloud Systems

Grouping-Based Reliable Privacy Preservation for Blockchain-Assisted Data Aggregation in Mobile Crowdsensing

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Verifiable, Reliable, and Privacy-Preserving Data Aggregation in Fog-Assisted Mobile Crowdsensing

Cited by 24 publications

References 45 publications

Two phase secure data collection technique for wireless sensor networks

Two phase secure data collection technique for wireless sensor networks

STQ-SCS: An Efficient and Secure Scheme for Fine-Grained Spatial-Temporal Top- k Query in Fog-Based Mobile Sensor-Cloud Systems

Grouping-Based Reliable Privacy Preservation for Blockchain-Assisted Data Aggregation in Mobile Crowdsensing

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STQ-SCS: An Efficient and Secure Scheme for Fine-Grained Spatial-Temporal Top- $k$ Query in Fog-Based Mobile Sensor-Cloud Systems