Towards utility-aware privacy-preserving sensor data anonymization in distributed IoT

2021 ◽  
Author(s):  
Xin Yang
Keyword(s):  
Privacy Preserving ◽  
Sensor Data ◽  
Data Anonymization

scholarly journals Privacy-Preserving Broker-ABE Scheme for Multiple Cloud-Assisted Cyber Physical Systems

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5463 ◽  
Author(s):  
Po-Wen Chi ◽  
Ming-Hung Wang
Keyword(s):  
Data Privacy ◽  
Service Providers ◽  
Real Life ◽  
Data Communication ◽  
Physical Space ◽  
Cloud Service ◽  
Cyber Physical Systems ◽  
Privacy Preserving ◽  
Sensor Data ◽  
Physical Systems

Cloud-assisted cyber–physical systems (CCPSs) integrate the physical space with cloud computing. To do so, sensors on the field collect real-life data and forward it to clouds for further data analysis and decision-making. Since multiple services may be accessed at the same time, sensor data should be forwarded to different cloud service providers (CSPs). In this scenario, attribute-based encryption (ABE) is an appropriate technique for securing data communication between sensors and clouds. Each cloud has its own attributes and a broker can determine which cloud is authorized to access data by the requirements set at the time of encryption. In this paper, we propose a privacy-preserving broker-ABE scheme for multiple CCPSs (MCCPS). The ABE separates the policy embedding job from the ABE task. To ease the computational burden of the sensors, this scheme leaves the policy embedding task to the broker, which is generally more powerful than the sensors. Moreover, the proposed scheme provides a way for CSPs to protect data privacy from outside coercion.

scholarly journals Scalable, High-Performance, and Generalized Subtree Data Anonymization Approach for Apache Spark

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 589
Author(s):  
Sibghat Ullah Bazai ◽  
Julian Jang-Jaccard ◽  
Hooman Alavizadeh
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
Privacy Preserving ◽  
Experimental Results ◽  
Apache Spark ◽  
Memory Usage ◽  
Top Down ◽  
Data Utility ◽  
Data Anonymization ◽  
Algorithm Implementation

Data anonymization strategies such as subtree generalization have been hailed as techniques that provide a more efficient generalization strategy compared to full-tree generalization counterparts. Many subtree-based generalizations strategies (e.g., top-down, bottom-up, and hybrid) have been implemented on the MapReduce platform to take advantage of scalability and parallelism. However, MapReduce inherent lack support for iteration intensive algorithm implementation such as subtree generalization. This paper proposes Distributed Dataset (RDD)-based implementation for a subtree-based data anonymization technique for Apache Spark to address the issues associated with MapReduce-based counterparts. We describe our RDDs-based approach that offers effective partition management, improved memory usage that uses cache for frequently referenced intermediate values, and enhanced iteration support. Our experimental results provide high performance compared to the existing state-of-the-art privacy preserving approaches and ensure data utility and privacy levels required for any competitive data anonymization techniques.

scholarly journals MCEEP-BDA: Multilevel Clustering Based -Energy Efficient Privacy-Preserving Big Data Aggregation in Large-Scale Wsn

2019 ◽  
Vol 9 (1) ◽  
pp. 6779-6785
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Large Scale ◽  
Computational Cost ◽  
Privacy Preserving ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Communication Overhead

In current scenario, the Big Data processing that includes data storage, aggregation, transmission and evaluation has attained more attraction from researchers, since there is an enormous data produced by the sensing nodes of large-scale Wireless Sensor Networks (WSNs). Concerning the energy efficiency and the privacy conservation needs of WSNs in big data aggregation and processing, this paper develops a novel model called Multilevel Clustering based- Energy Efficient Privacy-preserving Big Data Aggregation (MCEEP-BDA). Initially, based on the pre-defined structure of gradient topology, the sensor nodes are framed into clusters. Further, the sensed information collected from each sensor node is altered with respect to the privacy preserving model obtained from their corresponding sinks. The Energy model has been defined for determining the efficient energy consumption in the overall process of big data aggregation in WSN. Moreover, Cluster_head Rotation process has been incorporated for effectively reducing the communication overhead and computational cost. Additionally, algorithm has been framed for Least BDA Tree for aggregating the big sensor data through the selected cluster heads effectively. The simulation results show that the developed MCEEP-BDA model is more scalable and energy efficient. And, it shows that the Big Data Aggregation (BDA) has been performed here with reduced resource utilization and secure manner by the privacy preserving model, further satisfying the security concerns of the developing application-oriented needs.

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