A privacy-aware data sharing framework for Internet of Things through edge computing platform

Due to the wide adoption and deployment of the Internet of Things (IoT), massive amounts of data are being generated and shared across various sectors. Privacy disclosure is a major threat in IoT-related applications if collected data is directly outsourced. In IoT environments with large datasets, the existing Privacy-Preserving Data Mining (PPDM) mechanisms are inefficient and not scalable. To deal with this shortcoming, we develop a novel evolutionary PPDM framework, namely GPU-Enabled PPDM for IoT (GEPI), using GPUs at the edge layer to make the PPDM both efficient and exhibiting usefulness for IoT applications. On the one hand, the evolutionary algorithm used in the PPDM can guarantee the high utility by selecting the best candidate transactions for modification, providing a shareable dataset with minimum modifications and maximum privacy. On the other hand, the evolutionary algorithm is parallelized using a developed GPU based mechanism to accelerate database scans. In our mechanism, the dataset is distributed among the GPU threads to compute the fitness value in a parallel manner. Tests with extensive benchmarks reveal that our mechanism can accelerate the fitness function step 53.7x on average. The findings also show that the developed PPDM algorithm achieves an average speedup of 43.8x and 47.3x when compared to the state-of-the-art algorithms of ABC4ARH and PACO2DT, respectively.

[1]  Murali Annavaram,et al.  DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware , 2021, MICRO.

[2]  Asadollah Shahbahrami,et al.  High-performance implementation of evolutionary privacy-preserving algorithm for big data using GPU platform , 2021, Inf. Sci..

[3]  Xingxing Xiong,et al.  A Survey of Recent Advances in Edge-Computing-Powered Artificial Intelligence of Things , 2021, IEEE Internet of Things Journal.

[4]  Siguang Chen,et al.  Edge Blockchain Assisted Lightweight Privacy-Preserving Data Aggregation for Smart Grid , 2021, IEEE Transactions on Network and Service Management.

[5]  David J. Wu,et al.  CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU , 2021, 2021 IEEE Symposium on Security and Privacy (SP).

[6]  Gautam Srivastava,et al.  Privacy-Preserving Multiobjective Sanitization Model in 6G IoT Environments , 2021, IEEE Internet of Things Journal.

[7]  Gautam Srivastava,et al.  Hiding sensitive information in eHealth datasets , 2021, Future Gener. Comput. Syst..

[8]  Geyong Min,et al.  Lightweight Privacy-Preserving Scheme Using Homomorphic Encryption in Industrial Internet of Things , 2021, IEEE Internet of Things Journal.

[9]  Gautam Srivastava,et al.  Security protocol of sensitive high utility itemset hiding in shared IoT environments , 2021 .

[10]  Gautam Srivastava,et al.  An evolutionary computation‐based privacy‐preserving data mining model under a multithreshold constraint , 2021, Trans. Emerg. Telecommun. Technol..

[11]  Amir H. Gandomi,et al.  A survey of evolutionary computation for association rule mining , 2020, Inf. Sci..

[12]  Ainuddin Wahid Abdul Wahab,et al.  Systematic Review on Security and Privacy Requirements in Edge Computing: State of the Art and Future Research Opportunities , 2020, IEEE Access.

[13]  Amir H. Gandomi,et al.  Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony , 2020, Expert Syst. Appl..

[14]  Yang Lu,et al.  Privacy-Preserving and Pairing-Free Multirecipient Certificateless Encryption With Keyword Search for Cloud-Assisted IIoT , 2020, IEEE Internet of Things Journal.

[15]  Philippe Fournier-Viger,et al.  Hiding sensitive itemsets with multiple objective optimization , 2019, Soft Computing.

[16]  Jeff Druce,et al.  Privacy preserving Neural Network Inference on Encrypted Data with GPUs , 2019, ArXiv.

[17]  Sherman S. M. Chow,et al.  Goten: GPU-Outsourcing Trusted Execution of Neural Network Training , 2019, AAAI.

[18]  Geeta S. Navale,et al.  A multi-analysis on privacy preservation of association rules using hybridized approach , 2019, Evolutionary Intelligence.

[19]  Nankun Mu,et al.  Privacy preserving frequent itemset mining: Maximizing data utility based on database reconstruction , 2019, Comput. Secur..

[20]  H. Surendra,et al.  Hiding sensitive itemsets without side effects , 2019, Applied Intelligence.

[21]  Jimmy Ming-Tai Wu,et al.  A Sanitization Approach to Secure Shared Data in an IoT Environment , 2019, IEEE Access.

[22]  Jin Wang,et al.  Location Privacy Protection Based on Differential Privacy Strategy for Big Data in Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[23]  Haizhou Li,et al.  A Cost-Sensitive Deep Belief Network for Imbalanced Classification , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[24]  Keke Gai,et al.  Privacy-Preserving Content-Oriented Wireless Communication in Internet-of-Things , 2018, IEEE Internet of Things Journal.

[25]  Asadollah Shahbahrami,et al.  Data sanitization in association rule mining: An analytical review , 2018, Expert Syst. Appl..

[26]  Jianhua Chen,et al.  Certificateless Searchable Public Key Encryption Scheme for Industrial Internet of Things , 2018, IEEE Transactions on Industrial Informatics.

[27]  Asadollah Shahbahrami,et al.  Optimizing association rule hiding using combination of border and heuristic approaches , 2017, Applied Intelligence.

[28]  Justin Zhan,et al.  Ant Colony System Sanitization Approach to Hiding Sensitive Itemsets , 2017, IEEE Access.

[29]  Tzung-Pei Hong,et al.  A sanitization approach for hiding sensitive itemsets based on particle swarm optimization , 2016, Eng. Appl. Artif. Intell..

[30]  J. Roddick,et al.  Privacy preservation through a greedy, distortion-based rule-hiding method , 2016, Applied Intelligence.

[31]  T. Hong,et al.  The GA-based algorithms for optimizing hiding sensitive itemsets through transaction deletion , 2015, Applied Intelligence.

[32]  Laurence T. Yang,et al.  Data Mining for Internet of Things: A Survey , 2014, IEEE Communications Surveys & Tutorials.

[33]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[34]  Ali Amiri,et al.  Dare to share: Protecting sensitive knowledge with data sanitization , 2007, Decis. Support Syst..

[35]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[36]  S. Mali,et al.  Lossless and robust privacy preservation of association rules in data sanitization , 2018, Cluster Computing.