Encrypted Data Aggregation Research Articles

EFTA: An Efficient and Fault-Tolerant Data Aggregation Scheme without TTP in Smart Grid

Abstract With the rapid construction and implementation of smart grid, lots of studies have been conducted to explore how to ensure the security of information privacy. At present, most privacy-preserving data aggregation schemes in smart grid achieve privacy data protection through homomorphically encrypted data aggregation. However, these data aggregation schemes tend to rely on a trusted third party (TTP), and fail to efficiently handle the case of a meter failure. Besides, they are less flexible for overall user management, and resistance to collusion attacks needs to be improved. In this paper, we propose an efficient and robust privacy-preserving data aggregation scheme without TTP, called EFTA. Overall, the scheme eliminates the reliance on a TTP, combines with Shamir threshold secret sharing scheme to increase overall fault tolerance, supports flexible and dynamic user management, and effectively defends against entity initiated collusion attacks. According to security and performance analysis results, the scheme proposed in this paper meets the multiple security requirements of smart grid, and is more efficient in terms of overall overhead compared to the existing privacy-preserving data aggregation schemes.

Lightweight Multidimensional Encrypted Data Aggregation Scheme With Fault Tolerance for Fog-Assisted Smart Grids

Encrypted data aggregation is an effective approach to preserve power consumption data confidentiality as well as reduce communication overhead in smart grids. As the core component of smart grids, smart meters periodically report encrypted multidimensional data to an aggregator gateway. However, heavy computation and communication overhead may hinder the wide applications of smart meters. Besides, once the secret key is leaked, encrypted data will be broken, thereby violating users’ privacy. In this article, we present a lightweight multidimensional encryption data aggregation (MD-EDA) scheme, enabling a grid control center to retrieve fine-grain aggregated data in a confidential way. In particular, each fog node is installed at the edge of networks, playing the role of aggregator gateway for data verification and aggregation in the same grid area. MD-EDA prevents adversaries from extracting valuable data even if the secret key is compromised. MD-EDA exploits Shamir secret-sharing technique to achieve transmission fault tolerance mechanism from smart meters to a corresponding fog node. The performance evaluation demonstrates the lightweight computation advantages of MD-EDA in the deployment of fog-assisted smart grids.

Privacy-preserving statistical analysis over multi-dimensional aggregated data in edge computing-based smart grid systems

Smart grid systems enable bidirectional data communication between users and a smart grid control center (CC), by utilizing various communication infrastructures and embedded devices. To extract valuable information from users’ power consumption data efficiently, multi-dimensional data of users are required to be analyzed deeply. To protect users’ privacy, power consumption data are usually encrypted before transmission, which simultaneously makes it difficult to conduct statistical analysis. In this paper, we propose a scheme which enables privacy-preserving statistical analysis over multi-dimensional aggregated data (SA-MAD) in smart grid systems equipped with edge computing. We modify Boneh–Goh–Nissim (BGN) public key cryptosystem to a dual-message encryption mode, combining with two special superincreasing sequences to deal with multi-dimensional encrypted data aggregation. Besides, we design an identity-based aggregate signature to ensure encrypted data integrity in smart grid systems, and employ shamir secret sharing technique to support transmission fault-tolerance mechanism from smart meters to corresponding edge servers. SA-MAD enables CC to flexibly conduct privacy-preserving statistical analysis (e.g., sum, average, and variance) over aggregated data, and it could be easily extended to support covariance and linear regression computation. The performance evaluation demonstrates the feasibility of SA-MAD in edge computing-based smart grid systems.

Lightweight Aggregated Data Encryption for Wireless Sensor Networks (WSNs)

The primary task of wireless sensor networks (WSNs) is to collect various types of data, for example, weather data, traffic data, etc. Collected data in WSNs are different from most data transmitted in digital communication applications. Most collected data in WSNs contain only few bits of information, but most data in data communication applications contain a large number of bits. All existing data encryptions in WSNs use conventional symmetric-key encryptions (e.g., advanced encryption standard (AES)) in which each large-size key is used to encrypt a block of large-size of data (e.g., a fixed block size of 128 bit in AES), and the security is based on a large-size of pairwise shared key (e.g., 128/192/ 256 bits in AES) between the sender node and the receiver node. Obviously, these conventional encryptions are not suitable for WSNs since the size of keys are much larger than the size of data. We propose a novel data encryption, in which it encrypts only few bits of data, and the security is based on multiple pairwise shared keys with short length. The encryption speed is much faster than conventional symmetric encryptions. Data aggregation is an efficient way to transmit data in a network. In the past, most encrypted data aggregations were designed based on public-key based homomorphic encryption. Public-key encryptions need time-consuming computation, so these schemes are not suitable for applications in WSNs. Most data in WSNs are routed through a path from the source node to the receiver node, which involves multiple sensor nodes. Our encrypted data aggregation is also called path-oriented data encryption. When multiple data are collected in WSNs, our scheme allows sensors in the path to aggregate all collected data in a piggyback ride. Our proposed encrypted data aggregation is a lightweight solution, which is suitable for applications in WSNs.

Key-Leakage Resilient Encrypted Data Aggregation With Lightweight Verification in Fog-Assisted Smart Grids

In this article, we analyze the inherent characteristics of smart grids, and point out that some electricity consumption data are very sensitive and should be encrypted. However, once the corresponding private key is compromised, the content of encrypted data would be leaked, thereby violating users' privacy. Additionally, since a control center (CC) is always required to conduct accurate statistic analysis on these data for subsequent services, it is highly demanded for CC to check the integrity of encrypted data. To this end, based on a modified Boneh-Goh-Nissim (BGN) cryptosystem, we propose a key-leakage resilient encrypted data aggregation (KLR-EDA) scheme with lightweight verification in fog-assisted smart grids. KLR-EDA enables each fog node to aggregate first-level verifiable encrypted data from smart meters in the same grid area, and forward them to the cloud server (CS) for long-term storage. Upon receiving flexible challenging list of fog nodes from CC, CS produces second-level verifiable encrypted aggregated data and returns the results to CC. KLR-EDA enables CC to check the integrity of encrypted aggregated data efficiently, and further obtain the statistic analysis results on the aggregated data without learning any information of individual user. In particular, even the private key of CC is exposed or compromised, any adversary cannot break users' privacy. We provide security analysis of KLR-EDA, and conduct performance evaluation to demonstrate its lightweight statistical analysis and verification advantages on the CC side.

Aggregation Tree Statistical Computing Based on Functional Encryption

The medical monitoring system is widely used. In the medical monitoring system, each user only possesses one piece of data logging that participates in statistical computing. Specifically in such a situation, a feasible solution is to scatter its statistical computing workload to corresponding statistical nodes. Moreover, there are still two problems that should be resolved. One is how the server takes advantage of intermediate results obtained through statistical node aggregation to perform statistical computing. Statistical variable decomposition technique points out the direction for statistical projects. The other problem is how to design an efficient topological structure for statistical computing. In this paper, tree topology was adopted to implement data aggregation to improve aggregation efficiency. And two experiments were done for time consumption of statistical computing which focuses on encrypted data aggregation and encrypted data computing. The first experiment indicates that encrypted data aggregation efficiency of the scheme proposed in this paper is better than that of Drosatos’ scheme, and the second indicates that improving computing power of the server or computational efficiency of the functional encryption scheme can shorten the computation time.

Wireless Networks and Security Algorithms for Efficient Data Encryption in Smart Healthcare

Wireless sensor networks are encompassed with diversity tools of information technology which is widely applied in several domains including military surveillance, weather forecasting, earthquake forecasting and healthcare. Strengthened grounds are always developed for wireless sensor networks, which usually emerge security issues during professional application. Thus, essential technological tools are necessary to be assessed for a secured aggregation of data. Aggregation of encrypted data has been assessed through homomorphic stream cipher to assure its effectiveness. Methods: An experimental design has been incorporated, which utilized newly developed cipher along with CPU constrained devices. Modulation techniques have also been employed to evaluate the nature of aggregated data. Homomorphic stream cipher has been recognized as a simple and secure process, which has allowed efficient aggregation of encrypted data. Statistical values can be easily computed through the aggregation on the basis of selected cipher. The captured data from a healthcare industry, in accordance with variance, mean, and standard deviation has also been computed through the selected tool. It can be concluded that homomorphic stream cipher can be an ideal tool for appropriate aggregation of data.

Pallier Based Homomorphic Encrypted Data Aggregation in Wireless Sensor Networks

Tiny autonomous embedded electronics (sensor nodes) devices able to communicate through wireless channels are ensuring the emission and reception of data through a communication radio between two sensors grouped by hundreds and thousands within Wireless Sensor Networks (WSNs). These amazing new technology with ongoing research worldwide, are merging networking, systems hardware, systems software and programming methodologies thus enabling applications that previously were not practical. Hence numerical simulations on computers can now visualize the physical world phenomena that could be observed through empirical means, as sensors are deployed in a dedicated environment, to fulfill their aim of sensing for any occurrence of the event of interest. The data sensed by these wireless sensors are now very sensitive, thus need to be fully protected by all means, which is why T. D. Engouang et al., argued that securityand reliability and also durability are mandatory when deploying any sensor nodes or hard device. The Pallier based homomorphic encryption data aggregation is proposed with security measures preserving data integrity and privacy.

Secured and Encrypted Data Aggregation with Message Authentication Code in Wireless Sensor Networks

In wireless sensor networks, most of the application requires the transmission of data to base station in a secured manner. The existing techniques necessitate multi-hop forwarding that increases the cost and power utility. Also there is a chance for the malicious nodes to inject the false data during data aggregation and data forwarding. In order to overcome these issues, in this paper, we propose a secured and encrypted data aggregation with message authentication code in wireless sensor network. Initially the aggregator nodes are chosen based on the nodes connectivity. When the data aggregation is in process in the aggregation phase, where the encryption key and the verification key is assigned to the nodes while transmitting to the data aggregator, the MAC is also calculated by aggregator and the monitoring nodes. This helps in verifying the data integrity and false data detection. The monitoring node selection is done by the aggregators. By simulation results, we show that the proposed approach improves the data privacy and also reduces the load and power consumption

RSAED: Robust and Secure Aggregation of Encrypted Data in Wireless Sensor Networks

Recently, secure in-network aggregation in wireless sensor networks becomes a challenge issue, there is an extensive research on this area due to the large number of applications where the sensors are deployed and the security needs. In the last few years, aggregation of encrypted data has been proposed in order to maintain secrecy between the sensors and the sink, so the end-to-end data confidentiality is provided. However, the data integrity was not addressed. In this paper, we propose RSAED that allows integrity verification at intermediate nodes, ensures the base station to receive ciphertexts which come only from legitimate nodes and also improves the efficiency. Through implementation results, we evaluate our scheme using computation and communication overhead.

Efficient and provably secure aggregation of encrypted data in wireless sensor networks

Wireless sensor networks (WSNs) are composed of tiny devices with limited computation and battery capacities. For such resource-constrained devices, data transmission is a very energy-consuming operation. To maximize WSN lifetime, it is essential to minimize the number of bits sent and received by each device. One natural approach is to aggregate sensor data along the path from sensors to the sink. Aggregation is especially challenging if end-to-end privacy between sensors and the sink (or aggregate integrity) is required. In this article, we propose a simple and provably secure encryption scheme that allows efficient additive aggregation of encrypted data. Only one modular addition is necessary for ciphertext aggregation. The security of the scheme is based on the indistinguishability property of a pseudorandom function (PRF), a standard cryptographic primitive. We show that aggregation based on this scheme can be used to efficiently compute statistical values, such as mean, variance, and standard deviation of sensed data, while achieving significant bandwidth savings. To protect the integrity of the aggregated data, we construct an end-to-end aggregate authentication scheme that is secure against outsider-only attacks, also based on the indistinguishability property of PRFs.

A power efficient aggregation of encrypted data in wireless sensor network

In-network data aggregation in wireless sensor network has been shown to improve scalability, prolong sensor network lifetimes and diminish computational demands. However, the node that plays the role of data aggregation will consume much more energy than common nodes and may quit the mission in advance due to energy exhausting because of taxing decryption and re-encryption operation: moreover, it will bring complex key management to ensure the security of the data and corresponding keys. This paper was designed specifically to address above problem based on the thought of privacy homomorphism. It can achieve the perfect security level equal to one-time pad with much lower energy consumption; moreover, it can be proved to resist the attack of node capture. Using the simulation and analysis, we show that our scheme consume the energy only about 21% of AED scheme.