A survey on Privacy Preserving Data Aggregation Schemes in People Centric Sensing Systems and Wireless Domains

Jansi, K. R.; Raja, S. V. Kasmir

doi:10.17485/ijst/2016/v9i37/102072

Cited by 5 publications

(3 citation statements)

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“…Further, the model was not attacks resistive. The survey work presented in [7], [8] and [9] discussed about the adversaries and countermeasures for various attacks on urban sensing network. The attack models and mitigation techniques were discussed with effective data distribution strategies [11].…”

Section: Related Workmentioning

confidence: 99%

A Reliable Cloud Based Framework for Large Scale Urban Sensing Systems

Jansi

Sandhia

KasmirRaja

2021

Advances in Parallel Computing

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The rising usage of smartphones and sensing models in today’s life induces the development of large scale urban sensing networks in communication concepts. With the efficient implementation of people centric mobility models, the personal communication devices of people are acting as the sensor nodes that are capable of sensing the human behaviours and participating in framing the smart protocol designs for smart cities. Without the involvement of mobile users or people, the new protocols for mobility management, traffic management, environmental sensing and other applications become futile. The proposed cloud based model improves the reliability and scalability of the system with its multiple cloud servers design. The single point of failure can be resisted since many cloud servers belonging to a provider is used. So the framework remains fault tolerant in the presence of any server attacks. A standard homomorphic based encryption scheme is used for providing data confidentiality and also data is transferred anonymously improving the privacy of the system. The data aggregation process is supported by the model protecting the user’s privacy. The performance analysis for the proposed framework is done in terms of design goal analysis and computation cost analysis.

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Section: Related Workmentioning

confidence: 99%

A Reliable Cloud Based Framework for Large Scale Urban Sensing Systems

Jansi

Sandhia

KasmirRaja

2021

Advances in Parallel Computing

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“…In the first phase, each mobile device, divides the collected data into "n" portions as, respectively, where, "n" denotes the number of mobile devices that are presented in the network, which can be stated as, (1) In the second phase, the divided portions of data are swapped with each other. The portion is secured by , when others are distributed.…”

Section: Data Division Processmentioning

confidence: 99%

“…In the current decade, with the enormous increase of smart phones and mobiles, an efficient network is required for effective sensing of the corresponding environment and providing communication between the people. This can be achieved with the People Centric Sensing Networks (PCSN) [1], which involves in performing sensing based computations, storage and interaction between people and the environment with appropriate data collection, processing and data transmission functions. The advanced networking model PCSN are used in environmental observation [2], congestion control [3] and network traffic managements [4].…”

Section: Introductionmentioning

confidence: 99%

Towards Developing Secure Data Aggregation with Integrity Verification Model (SDA-IV) in People Centric Sensing Systems

2019

IJEAT

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In present scenario of vast developments in wireless communication methods, embedded device based operations and mobile communications, sensor based techniques are widely adopted. Such systems are termed as People Centric Sensing Systems, which become very popular and acquires greater attention of researchers recently. However, security and privacy in transmitting data has been the major issue in People Centric Sensing Network (PCSN). For handling that problem efficiently, this paper presents a model called Security Data Aggregation and Integrity Verification (SDA-IV) model for providing privacy preserved data sharing between devices in PCSN. A new peer-to-peer oriented secure data sharing is achieved with the proposed model by making the user to shares their data randomly with other nodes along with the incorporating of integrity checking conceit. Moreover, the work comprises four phases: Initial Setup, Data Division and Encryption, Data Aggregation and Integrity Verification. Homomorphic Message Authentication Code is user for privacy preserving process and Hashing Functions are incorporated for integrity verification of shared data. Furthermore, the efficiency of the proposed SDA-IC model is evidenced using simulation results and comparative evaluations.

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People-centric collective intelligence: decentralized and enhanced privacy mobile crowd sensing based on blockchain

Arulprakash

Jebakumar

2021

J Supercomput

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A survey on Privacy Preserving Data Aggregation Schemes in People Centric Sensing Systems and Wireless Domains

Cited by 5 publications

References 0 publications

A Reliable Cloud Based Framework for Large Scale Urban Sensing Systems

A Reliable Cloud Based Framework for Large Scale Urban Sensing Systems

Towards Developing Secure Data Aggregation with Integrity Verification Model (SDA-IV) in People Centric Sensing Systems

People-centric collective intelligence: decentralized and enhanced privacy mobile crowd sensing based on blockchain

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