Chameleon: a Blind Double Trapdoor Hash Function for Securing AMI Data Aggregation

Data aggregation is an integral part of Advanced Metering Infrastructure (AMI) deployment that is implemented by the concentrator. Data aggregation reduces the number of transmissions, thereby reducing communication costs and increasing the bandwidth utilization of AMI. However, the concentrator poses a great risk of being tampered with, leading to erroneous bills and possible consumer disputes. In this paper, we propose an end-to-end integrity protocol using elliptic curve based chameleon hashing to provide data integrity and authenticity. The concentrator generates and sends a chameleon hash value of the aggregated readings to the Meter Data Management System (MDMS) for verification, while the smart meter with the trapdoor key computes and sends a commitment value to the MDMS so that the resulting chameleon hash value calculated by the MDMS is equivalent to the previous hash value sent by the concentrator. By comparing the two hash values, the MDMS can validate the integrity and authenticity of the data sent by the concentrator. Compared with the discrete logarithm implementation, the ECC implementation reduces the computational cost of MDMS, concentrator and smart meter by approximately 36.8%, 80%, and 99% respectively. We also demonstrate the security soundness of our protocol through informal security analysis

Fault-Tolerant Secure Data Aggregation Schemes in Smart Grids: Techniques, Design Challenges, and Future Trends

Secure data aggregation is an important process that enables a smart meter to perform efficiently and accurately. However, the fault tolerance and privacy of the user data are the most serious concerns in this process. While the security issues of Smart Grids are extensively studied, these two issues have been ignored so far. Therefore, in this paper, we present a comprehensive survey of fault-tolerant and differential privacy schemes for the Smart Gird. We selected papers from 2010 to 2021 and studied the schemes that are specifically related to fault tolerance and differential privacy. We divided all existing schemes based on the security properties, performance evaluation, and security attacks. We provide a comparative analysis for each scheme based on the cryptographic approach used. One of the drawbacks of existing surveys on the Smart Grid is that they have not discussed fault tolerance and differential privacy as a major area and consider them only as a part of privacy preservation schemes. On the basis of our work, we identified further research areas that can be explored

The Theory and Application of Privacy-preserving Computation

Privacy is a growing concern in the digital world as more information becomes digital every day. Often the implications of how this information could be exploited for nefarious purposes are not explored until after the fact. The public is becoming more concerned about this. This dissertation introduces a new paradigm for tackling the problem, namely, transferable multiparty computation (T-MPC). T-MPC builds upon existing multiparty computation work yet allows some additional flexibility in the set of participants. T-MPC is orders of magnitude more efficient for certain applications. This greatly increases the scalability of the sizes of networks supported for privacy-preserving computation