Internet of Things (IoT) facilitates intelligent interconnection and data exchange between devices. However, existing data aggregation schemes face challenges such as edge node disconnections, high computational overhead, and limited fault tolerance, which affect the reliability and efficiency of the system. To address these issues, this paper proposes a smart contract assisted and fault-tolerant data aggregation scheme without a trusted authority, named Cap. To realize it, we integrate blockchain technology and homomorphic encryption. Through smart contracts, we dynamically select and switch edge nodes to ensure that the data aggregation process continues even if some nodes go offline. Moreover, by leveraging homomorphic secret sharing, we effectively minimize communication overhead and ensure that users can exit the aggregation process without jeopardizing data integrity. This scheme provides an efficient and reliable solution for secure data aggregation in Iot environments and enhances the robustness and data security of the system. Performance evaluation show that, compared with existing schemes, Cap reduces computational and communication overhead by 30%.

Privacy-preserving multidimensional data aggregation aggregates the data of all different users into a single value, preventing the leakage of personal data while ensuring its availability. However, most current multidimensional data aggregation schemes only consider sum operations, i.e., the message of each dimension in the aggregation result is the sum of the corresponding dimensional messages of all individual message vectors. We propose a multidimensional data aggregation scheme with fine-grained linear homomorphism, called MDA-FLH. Firstly, we construct a fine-grained linear homomorphic encryption scheme which can assign different weights to each dimension of user’s data and maintain the linear homomorphic property in each dimension. We combine the Chinese remainder theorem and Paillier encryption to encode the multidimensional data with CRT and assign different weights to each dimension of user’s data in the Paillier ciphertext. Secondly, our scheme has the property of fault tolerance. In conjunction with extended Shamir’s threshold secret-sharing scheme, a security-enhanced and fault-tolerant data aggregation method has been designed so that it is resistant to internal attacks such as the control center (CC) access to individual private data if given the corresponding ciphertext. Finally, two practical schemes are designed based on MDA-FLH: fine-grained electricity price statistics scheme and multistep electricity price statistics scheme. Security analysis shows that our scheme can achieve privacy, confidentiality, integrity, and source authentication. Performance analysis shows that our scheme is efficient, especially that SMs are computationally economic, which makes our scheme more suitable for resource-constrained SM. Also, our scheme can provide linear homomorphism operations on each dimension, which further expands its applications.

With the rapid development of smart grids (SGs), designing a data aggregation scheme that ensures both data availability and privacy security has become an urgent necessity. Particularly, considering the potential failures of smart meters (SMs), ensuring fault tolerance in data decryption has become a significant challenge in the design. Recently, Wu et al. proposed a fault-tolerant data aggregation scheme FPDA that excels in privacy protection and fault tolerance. However, like most data aggregation schemes in SG, FPDA relies on a trust authority (TA), which is difficult to find in real-world scenarios. Furthermore, the scheme is vulnerable to delay attacks, posing a risk of individual meter privacy leakage. Therefore, this article first designs an attack for FPDA scheme which capable of achieving 100% plaintext recovery when users delays, with an attack time of only 518 ms. Subsequently, we propose a secure fault-tolerant and privacy-enhanced data aggregation scheme for SG without TA (SFPDA). By employing multiuser Diffie-Hellman key exchange (MDHKE), we eliminate the need for a trusted third party, Additionally, we utilize dual masking to resist delay attacks. We conduct a security analysis of the scheme, which demonstrates that SFPDA can resist delay attacks and provide enhanced privacy protection. Finally, experimental results show that SFPDA reduces encryption time by approximately 40% and decryption time by approximately 83.3%, while offering better fault tolerance, making it more suitable for grid environments where the number of residents remains constant, but the failure rate of meters fluctuates.

Privacy-preserving data aggregation (PPDA) enables data availability and privacy preservation simultaneously in smart grid. However, existing methods, such as masking and homomorphic encryption, cannot simultaneously offer strong privacy preservation, fault tolerance for both smart meters and aggregators, verifiable aggregation, and lightweight encryption. To tackle these challenges, we design HTV-PRE, a homomorphic threshold proxy re-encryption scheme with re-encryption verifiability. HTV-PRE involves only linear operations and resists quantum attacks after being instanced by ideal lattices. By leveraging HTV-PRE, we propose a robust and lightweight data aggregation scheme with strong privacy preservation for smart grid. Robustness ensures fault tolerance and error detection. Even if some smart meters or aggregators are faulty, data aggregation can still work without imposing expensive computation on other smart meters or requiring additional trust assumptions. Additionally, to detect aggregators' errors, a proof for the aggregated result is presented so that anyone can verify whether the result has been correctly computed or not. The verifiable aggregation adds no computation/communication overhead on the user side. The performance evaluations demonstrate that our PPDA scheme significantly offloads computation overhead from smart meters and control center to the edge, and its user encryption is up to 4x faster than existing approaches.

Short-term residential electrical load forecasting (SRLF) as a cloud service usually requires fine-grained electricity consumption data as input. However, those data are closely related to users' lifestyles, thus bringing about privacy concerns. We adapt homomorphic encryption into temporal convolutional networks (TCN) to yield an efficient design for SRLF, named SecTCN, which preserves privacy for both user data and model parameters. First, a homomorphic-encryption-friendly model is proposed through novel Ticktock approximations. Second, secure load forecasting over the encrypted data is executed by cloud–edge collaboration. Third, a novel data representation and related ciphertext computations are proposed to accelerate forecasting, and a position shuffler is devised to protect models from equation-solving attacks. Experimental evaluations demonstrate that SecTCN reduces a root-mean-squared error by 21.75 averagely and a mean absolute percentage error by 4.22%$\text{ to } $22.16%, compared to unencrypted long short-term memory (LSTM) and TCN. On average, SecTCN requires only 1.10 s to make forecasting with 10.27 MB communication traffic.

The data aggregation approach in smart grid (SG) is an effective solution to make data available while keeping privacy preserving at the same time. The fault tolerance means decryption can still be carried out successfully even if some smart meters (SMs) are breaking down. It is a challenging issue to design an efficient, fault-tolerant data aggregation scheme with no help of centralized trusted authority (TA) or key update after each fault recovery. Recently, a fault-tolerant data aggregation scheme FESDA (Saleemet al.,2020) was presented. However, we identify a serious and inherent vulnerability in its fault tolerance. Specifically, given an equivalent ciphertext derived from each SM’s private key aiming at resiting faults, the control center can abuse it to obtain any SM’s reading. An effective attack is launched with both theoretical proof and experimentative verification. Furthermore, to fix it and solve the challenging issue, we first design an extended Shamir’s threshold secret-sharing scheme (tSSS) with master secret security and reusability, allowing SMs to reconstruct subsequent multiple secrets without leaking their original secret shares. Then, if some SMs fail to submit data successfully, the fog node (FN) starts extra request–response interactivities among itself and a limited number of SMs. Finally, a privacy-enhanced aggregation of normal reports can also be achieved. Extensive experiments demonstrate that the majority of computation costs and communication overload to acquire fault tolerance are offload on FNs, while SMs are computationally economical.

Threshold proxy re-encryption (TPRE) can prevent collusion between a single proxy and a delegatee from converting arbitrary files against the wishes of the delegator through multiple proxies, and can also provide normal services even when certain proxy servers are paralyzed or damaged. A non-interactive identity-based TPRE (IB-TPRE) scheme over lattices is proposed which removes the public key certificates. To accomplish this scheme, Shamir’s secret sharing is employed twice, which not only effectively hides the delegator’s private key information, but also decentralizes the proxy power by splitting the re-encryption key. Robustness means that a combiner can detect a misbehaving proxy server that has sent an invalid transformed ciphertext share. This property is achieved by lattice-based fully homomorphic signatures. As a result, the whole scheme is thoroughly capable of resisting quantum attacks even when they are available. The security of the proposed scheme is based on the decisional learning with error hardness assumption in the standard model. Two typical application scenarios, including a file-sharing system based on a blockchain network and a robust key escrow system with threshold cryptography, are presented.