FESDA: Fog-Enabled Secure Data Aggregation in Smart Grid IoT Network

With advances in fog and edge computing, various problems such as data processing for large Internet of Things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid (SG) IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for SGs incur high computation and communication costs, and in recent years, there have been efforts to leverage fog computing with SGs to overcome these limitations. In this article, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of the Paillier cryptosystem is used to encrypt the consumption data of the smart meter (SM) users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the SMs malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption, and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the privacy-preserving fog-enabled data aggregation scheme.

[1]  Rajkumar Buyya,et al.  iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments , 2016, Softw. Pract. Exp..

[2]  Xiaodong Lin,et al.  Differentially Private Smart Metering With Fault Tolerance and Range-Based Filtering , 2017, IEEE Transactions on Smart Grid.

[3]  Georges Kaddoum,et al.  Toward Secure and Provable Authentication for Internet of Things: Realizing Industry 4.0 , 2020, IEEE Internet of Things Journal.

[4]  Carlos Juiz,et al.  YAFS: A Simulator for IoT Scenarios in Fog Computing , 2019, IEEE Access.

[5]  Sherali Zeadally,et al.  Lightweight and efficient privacy-preserving data aggregation approach for the Smart Grid , 2017, Ad Hoc Networks.

[6]  Abdul Hameed,et al.  Towards a set aggregation-based data integrity scheme for smart grids , 2017, Ann. des Télécommunications.

[7]  Marimuthu Palaniswami,et al.  PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid , 2018, IEEE Transactions on Industrial Informatics.

[8]  Madhuri Bhavsar,et al.  Influence of Montoring: Fog and Edge Computing , 2019, Scalable Comput. Pract. Exp..

[9]  Rongxing Lu,et al.  A lightweight data aggregation scheme achieving privacy preservation and data integrity with differential privacy and fault tolerance , 2017, Peer-to-Peer Netw. Appl..

[10]  Xiaodong Lin,et al.  A Novel Privacy-Preserving Set Aggregation Scheme for Smart Grid Communications , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[11]  Mohsen Guizani,et al.  Secure and Lightweight Authentication Scheme for Smart Metering Infrastructure in Smart Grid , 2020, IEEE Transactions on Industrial Informatics.

[12]  Hovav Shacham,et al.  Short Signatures from the Weil Pairing , 2001, J. Cryptol..

[13]  Alfred Menezes,et al.  Handbook of Applied Cryptography , 2018 .

[14]  Xiaodong Lin,et al.  Enabling Efficient and Privacy-Preserving Aggregation Communication and Function Query for Fog Computing-Based Smart Grid , 2020, IEEE Transactions on Smart Grid.

[15]  Kim-Kwang Raymond Choo,et al.  FGDA: Fine-grained data analysis in privacy-preserving smart grid communications , 2018, Peer Peer Netw. Appl..

[16]  Beibei Li,et al.  Distributed host-based collaborative detection for false data injection attacks in smart grid cyber-physical system , 2017, J. Parallel Distributed Comput..

[17]  Marek Klonowski,et al.  On practical privacy-preserving fault-tolerant data aggregation , 2018, International Journal of Information Security.

[18]  Jennifer Seberry,et al.  Public Key Cryptography , 2000, Lecture Notes in Computer Science.

[19]  M. Newborough,et al.  Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design , 2003 .

[20]  Zekeriya Erkin,et al.  A fault-tolerant and efficient scheme for data aggregation over groups in the smart grid , 2017, 2017 IEEE Workshop on Information Forensics and Security (WIFS).

[21]  Paul Rad,et al.  Implementation of deep packet inspection in smart grids and industrial Internet of Things: Challenges and opportunities , 2019, J. Netw. Comput. Appl..

[22]  Mohammad S. Obaidat,et al.  Fog Computing for Smart Grid Systems in the 5G Environment: Challenges and Solutions , 2019, IEEE Wireless Communications.

[23]  Wei Guo,et al.  A Practical Privacy-Preserving Data Aggregation (3PDA) Scheme for Smart Grid , 2019, IEEE Transactions on Industrial Informatics.

[24]  Peilin Hong,et al.  PPMA: Privacy-Preserving Multisubset Data Aggregation in Smart Grid , 2018, IEEE Transactions on Industrial Informatics.

[25]  Xi Fang,et al.  3. Full Four-channel 6.3-gb/s 60-ghz Cmos Transceiver with Low-power Analog and Digital Baseband Circuitry 7. Smart Grid — the New and Improved Power Grid: a Survey , 2022 .

[26]  Vitaly Ford,et al.  Secure and efficient protection of consumer privacy in Advanced Metering Infrastructure supporting fine-grained data analysis , 2017, J. Comput. Syst. Sci..

[27]  Pascal Paillier,et al.  Public-Key Cryptosystems Based on Composite Degree Residuosity Classes , 1999, EUROCRYPT.

[28]  Beibei Li,et al.  On Reliability Analysis of Smart Grids under Topology Attacks , 2018, ACM Trans. Cyber Phys. Syst..

[29]  Josep Domingo-Ferrer,et al.  Anonymous and secure aggregation scheme in fog-based public cloud computing , 2018, Future Gener. Comput. Syst..

[30]  Rongxing Lu,et al.  DDPFT: Secure data aggregation scheme with differential privacy and fault tolerance , 2015, 2015 IEEE International Conference on Communications (ICC).

[31]  Xuemin Sherman Shen,et al.  A Lightweight Lattice-Based Homomorphic Privacy-Preserving Data Aggregation Scheme for Smart Grid , 2018, IEEE Transactions on Smart Grid.

[32]  Yue Zhang,et al.  APPA: An anonymous and privacy preserving data aggregation scheme for fog-enhanced IoT , 2019, J. Netw. Comput. Appl..

[33]  Hugo Krawczyk,et al.  Keying Hash Functions for Message Authentication , 1996, CRYPTO.