scholarly journals Two Secure Privacy-Preserving Data Aggregation Schemes for IoT

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yuwen Pu ◽  
Jin Luo ◽  
Chunqiang Hu ◽  
Jiguo Yu ◽  
Ruifeng Zhao ◽  
...  
Keyword(s):  
Data Aggregation ◽  
Secure Communication ◽  
Homomorphic Encryption ◽  
Computational Cost ◽  
Health Condition ◽  
Security And Privacy ◽  
Actual Data ◽  
Smart Healthcare ◽  
Private Data ◽  
Noise Data

As the next generation of information and communication infrastructure, Internet of Things (IoT) enables many advanced applications such as smart healthcare, smart grid, smart home, and so on, which provide the most flexibility and convenience in our daily life. However, pervasive security and privacy issues are also increasing in IoT. For instance, an attacker can get health condition of a patient via analyzing real-time records in a smart healthcare application. Therefore, it is very important for users to protect their private data. In this paper, we present two efficient data aggregation schemes to preserve private data of customers. In the first scheme, each IoT device slices its actual data randomly, keeps one piece to itself, and sends the remaining pieces to other devices which are in the same group via symmetric encryption. Then, each IoT device adds the received pieces and the held piece together to get an immediate result, which is sent to the aggregator after the computation. Moreover, homomorphic encryption and AES encryption are employed to guarantee secure communication. In the second scheme, the slicing strategy is also employed. Noise data are introduced to prevent the exchanged actual data of devices from disclosure when the devices blend data each other. AES encryption is also employed to guarantee secure communication between devices and aggregator, compared to homomorphic encryption, which has significantly less computational cost. Analysis shows that integrity and confidentiality of IoT devices’ data can be guaranteed in our schemes. Both schemes can resist external attack, internal attack, colluding attack, and so on.

scholarly journals Secure Data Aggregation Scheme for IOT Applications in Cloud

2020 ◽  
Vol 9 (4S2) ◽  
pp. 70-76
Keyword(s):  
Cloud Computing ◽  
Data Hiding ◽  
Data Aggregation ◽  
Dynamic Network ◽  
Security And Privacy ◽  
Iot Applications ◽  
P Gene ◽  
Private Data ◽  
Aggregation Scheme ◽  
And Storage

While Internet of Things (IoT) technology comprises of nodes that are self-configuring and intelligent which are interconnected in a dynamic network, utilization of shared resources has been revolutionized by the cloud computing effectively reducing the cost overheadamong the cloud users.The major concerns of IoT infrastructure are reliability, performance, security and privacy. Cloud computing is popular for its unlimited storage and processing power. Cloud computing is much more matured with the capability to resolve most of the issues in IoT technology. A suitable way to address most of the issues in IoT technology is by integrating IoTparadigm into the Cloud technology.In this regard, we propose a methodology of applying our EPAS scheme for IoT applications. In our previous work[2] , we have proposed an Enhanced Privacy preserving gene based data Aggregation Scheme (EPAS) for private data transmission and storage by utilizing Enhanced P-Gene erasable data hiding approach. Enhanced P-Gene scheme ensures secure transmission and storage of private data by relying on a data aggregation scheme fully dependent on erasable data hiding technique. In the current work we analyse the applicability of the EPAS scheme for IoT applications. Experimental results show the suitability of the proposed scheme for application involving numeric data and also demonstrates performance improvement with existing proposals for data aggregation in cloud.

Secure Multi-Subinterval Data Aggregation Scheme with Interval Privacy Preservation for Vehicle Sensing Systems

2021 ◽  
pp. 2150286
Author(s):  
Peng Hu ◽  
Yongli Wang ◽  
Ahmadreza Vajdi ◽  
Bei Gong ◽  
Yongjian Wang
Keyword(s):  
Data Aggregation ◽  
Privacy Preservation ◽  
Homomorphic Encryption ◽  
Security Analysis ◽  
Security And Privacy ◽  
Communication Overhead ◽  
Network Resources ◽  
Sensing Systems ◽  
Aggregation Scheme ◽  
And Performance

Road side units (RSUs) can act as fog nodes to perform data aggregation at the edge of network, which can reduce communication overhead and improve the utilization of network resources. However, because the RSU is public infrastructure, this feature may bring data security and privacy risks in data aggregation. In this paper, we propose a secure multi-subinterval data aggregation scheme, named SMDA, with interval privacy preservation for vehicle sensing systems. Specifically, our scheme combines the [Formula: see text] encoding theory and proxy re-encryption to protect interval privacy, this can ensure that the interval information is only known by the data center, and the RSU can classify the encrypted data without knowing the plaintext of the data and interval information. Meanwhile, our scheme employs the Paillier homomorphic encryption to accomplish data aggregation at the RSU, and the Identity-based batch authentication technology to solve authentication and data integrity. Finally, the security analysis and performance evaluations illustrate the safety and efficiency of our scheme.

scholarly journals A Secure and Scalable Data Communication Scheme in Smart Grids

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Chunqiang Hu ◽  
Hang Liu ◽  
Liran Ma ◽  
Yan Huo ◽  
Arwa Alrawais ◽  
...  
Keyword(s):  
Access Control ◽  
Smart Grids ◽  
Secure Communication ◽  
Service Providers ◽  
Computational Cost ◽  
Data Communication ◽  
Security And Privacy ◽  
Smart Meters ◽  
Data Repositories ◽  
Utility Companies

The concept of smart grid gained tremendous attention among researchers and utility providers in recent years. How to establish a secure communication among smart meters, utility companies, and the service providers is a challenging issue. In this paper, we present a communication architecture for smart grids and propose a scheme to guarantee the security and privacy of data communications among smart meters, utility companies, and data repositories by employing decentralized attribute based encryption. The architecture is highly scalable, which employs an access control Linear Secret Sharing Scheme (LSSS) matrix to achieve a role-based access control. The security analysis demonstrated that the scheme ensures security and privacy. The performance analysis shows that the scheme is efficient in terms of computational cost.

scholarly journals EPPDA: An Efficient and Privacy-Preserving Data Aggregation Scheme with Authentication and Authorization for IoT-Based Healthcare

2021 ◽  
Author(s):  
Faris. A. Almalki ◽  
Ben othman Soufiene
Keyword(s):  
Data Aggregation ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Communication Overhead ◽  
Range Data ◽  
Aggregation Techniques ◽  
Aggregation Scheme ◽  
Iot Devices

Abstract Internet of Things (IoT) connects various kinds of intelligent objects and devices using the internet to collect and exchange data. Nowadays, The IoT is used in diverse application domains, including the healthcare. In the healthcare domain, the IoT devices can collects patient data, and its forwards the data to the healthcare professionals can view it. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range, data aggregation techniques are used to reduce the communication overhead. However, in healthcare system using IoT, the heterogeneity of technologies, the large number of devices and systems, and the different types of users and roles create important challenges in terms of security. For that, the security and privacy aggregation of health data are very important aspects. In this paper, we propose a novel secure data aggregation scheme based on homomorphic primitives in IoT based healthcare systems, called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based the Verification and Authorization phase to verifying the legitimacy of the nodes wants to join the process of aggregation. EPPDA uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

scholarly journals Private Data Aggregation Based on Fog-Assisted Authentication for Mobile Crowd Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ruyan Wang ◽  
Shiqi Zhang ◽  
Zhigang Yang ◽  
Puning Zhang ◽  
Dapeng Wu ◽  
...  
Keyword(s):  
Spatial Data ◽  
Data Aggregation ◽  
Interpolation Method ◽  
Homomorphic Encryption ◽  
Mobile Crowd Sensing ◽  
Transmission Method ◽  
Crowd Sensing ◽  
Sensing Platform ◽  
Private Data ◽  
Mobile Crowd

In mobile crowd sensing (MCS), the cloud as a single sensing platform undertakes a large number of communication tasks, leading to the reduction of sensing task execution efficiency and the risk of loss and leakage of users’ private data. In this paper, we propose a spatial ciphertext aggregation scheme with collaborative verification of fog nodes. Firstly, the cloud and fog collaboration architecture is constructed. Fog nodes are introduced for data validation and slices transmission, reducing computing cost on the sensing platform. Secondly, a multipath transmission method of slice data is proposed, in which the user identity and data are transmitted anonymously by the secret sharing method, and the data integrity is guaranteed by hash chain authentication. Finally, a spatial data aggregation method based on privacy protection is presented. The ciphertext aggregation calculation of the sensing platform is realized through Paillier homomorphic encryption, and the problem of insufficient data coverage in the sensing region is solved by the position-based weight interpolation method. The security analysis demonstrates that the scheme can achieve the expected security goal. The simulation results show the feasibility and effectiveness of the proposed scheme.

scholarly journals Privacy-Preserving IoT Data Aggregation Based on Blockchain and Homomorphic Encryption

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2452
Author(s):  
Faiza Loukil ◽  
Chirine Ghedira-Guegan ◽  
Khouloud Boukadi ◽  
Aïcha-Nabila Benharkat
Keyword(s):  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Aggregation Process ◽  
Smart Contract ◽  
Reasonable Choice ◽  
Iot Devices

Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers that can answer the consumer’s request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations. Therefore, group-level aggregation obfuscates IoT data, which complicates sensitive information inference from a single IoT device. Finally, we deploy the proposal on a private Ethereum blockchain and give the performance evaluation.

scholarly journals A novel fault-tolerant privacy-preserving cloud-based data aggregation scheme for lightweight health data

2021 ◽  
Vol 18 (6) ◽  
pp. 7539-7560
Author(s):  
Fawza A. Al-Zumia ◽  
◽  
Yuan Tian ◽  
Mznah Al-Rodhaan ◽  
Keyword(s):  
Data Aggregation ◽  
Differential Privacy ◽  
Fault Tolerant ◽  
Computational Cost ◽  
Health Data ◽  
Security And Privacy ◽  
Health Networks ◽  
Aggregated Data ◽  
Medical Health Care ◽  
Aggregation Scheme

<abstract> <p>Mobile health networks (MHNWs) have facilitated instant medical health care and remote health monitoring for patients. Currently, a vast amount of health data needs to be quickly collected, processed and analyzed. The main barrier to doing so is the limited amount of the computational storage resources that are required for MHNWs. Therefore, health data must be outsourced to the cloud. Although the cloud has the benefits of powerful computation capabilities and intensive storage resources, security and privacy concerns exist. Therefore, our study examines how to collect and aggregate these health data securely and efficiently, with a focus on the theoretical importance and application potential of the aggregated data. In this work, we propose a novel design for a private and fault-tolerant cloud-based data aggregation scheme. Our design is based on a future ciphertext mechanism for improving the fault tolerance capabilities of MHNWs. Our scheme is privatized via differential privacy, which is achieved by encrypting noisy health data and enabling the cloud to obtain the results of only the noisy sum. Our scheme is efficient, reliable and secure and combines different approaches and algorithms to improve the security and efficiency of the system. Our proposed scheme is evaluated with an extensive simulation study, and the simulation results show that it is efficient and reliable. The computational cost of our scheme is significantly less than that of the related scheme. The aggregation error is minimized from ${\rm{O}}\left( {\sqrt {{\bf{w + 1}}} } \right)$ in the related scheme to O(1) in our scheme.</p> </abstract>

Security in Smart Healthcare System: A Comprehensive Survey

2017 ◽  
Vol 7 (10) ◽  
pp. 39
Author(s):  
P. Jeyadurga ◽  
S. Ebenezer Juliet ◽  
I. Joshua Selwyn ◽  
P. Sivanisha
Keyword(s):  
Secure Communication ◽  
Smart Cities ◽  
Healthcare Systems ◽  
Internet Security ◽  
Security Risk ◽  
System A ◽  
Smart Healthcare ◽  
Comprehensive Survey ◽  
Healthcare Monitoring ◽  
The Internet Of Things

The Internet of things (IoT) is one of the emerging technologies that brought revolution in many application domains such as smart cities, smart retails, healthcare monitoring and so on. As the physical objects are connected via internet, security risk may arise. This paper analyses the existing technologies and protocols that are designed by different authors to ensure the secure communication over internet. It additionally focuses on the advancement in healthcare systems while deploying IoT services.

A fully homomorphic encryption based on magic number fragmentation and El‐Gamal encryption: Smart healthcare use case

2021 ◽  
Author(s):  
Mostefa Kara ◽  
Abdelkader Laouid ◽  
Mohammed Amine Yagoub ◽  
Reinhardt Euler ◽  
Saci Medileh ◽  
...  
Keyword(s):  
Homomorphic Encryption ◽  
Magic Number ◽  
Use Case ◽  
Healthcare Use ◽  
Fully Homomorphic Encryption ◽  
Smart Healthcare

scholarly journals Efficient Private Conjunctive Query Protocol Over Encrypted Data

Cryptography ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 2
Author(s):  
Tushar Kanti Saha ◽  
Takeshi Koshiba
Keyword(s):  
Homomorphic Encryption ◽  
Data Access ◽  
Conjunctive Query ◽  
Conjunctive Queries ◽  
Private Data ◽  
Binary Format ◽  
Main Technique ◽  
Batch Technique ◽  
Outsourced Database ◽  
Packing Method

Conjunctive queries play a key role in retrieving data from a database. In a database, a query containing many conditions in its predicate, connected by an “and/&/∧” operator, is called a conjunctive query. Retrieving the outcome of a conjunctive query from thousands of records is a heavy computational task. Private data access to an outsourced database is required to keep the database secure from adversaries; thus, private conjunctive queries (PCQs) are indispensable. Cheon, Kim, and Kim (CKK) proposed a PCQ protocol using search-and-compute circuits in which they used somewhat homomorphic encryption (SwHE) for their protocol security. As their protocol is far from being able to be used practically, we propose a practical batch private conjunctive query (BPCQ) protocol by applying a batch technique for processing conjunctive queries over an outsourced database, in which both database and queries are encoded in binary format. As a main technique in our protocol, we develop a new data-packing method to pack many data into a single polynomial with the batch technique. We further enhance the performances of the binary-encoded BPCQ protocol by replacing the binary encoding with N-ary encoding. Finally, we compare the performance to assess the results obtained by the binary-encoded BPCQ protocol and the N-ary-encoded BPCQ protocol.

Sign in / Sign up

Export Citation Format

Share Document