Towards secure end-to-end data aggregation in AMI through delayed-integrity-verification

The integrity and authenticity of the energy usage data in Advanced Metering Infrastructure (AMI) is crucial to ensure the correct energy load to facilitate generation, distribution and customer billing. Any malicious tampering to the data must be detected immediately. This paper introduces secure end-to-end data aggregation for AMI, a security protocol that allows the concentrators to securely aggregate the data collected from the smart meters, while enabling the utility back-end that receives the aggregated data to verify the integrity and data originality. Compromise of concentrators can be detected. The aggregated data is protected using Chameleon Signatures and then forwarded to the utility back-end for verification, accounting, and analysis. Using the Trapdoor Chameleon Hash Function, the smart meters can periodically send an evidence to the utility back-end, by computing an alternative message and a random value (m', r) such that m' consists of all previous energy usage measurements of the smart meter in a specified period of time. By verifying that the Chameleon Hash Value of (m', r) and that the energy usage matches those aggregated by the concentrators, the utility back-end is convinced of the integrity and authenticity of the data from the smart meters. Any data anomaly between smart meters and concentrators can be detected, thus indicating potential compromise of concentrators

Secure Data Provenance in Home Energy Monitoring Networks

Smart grid empowers home owners to efficiently manage their smart home appliances within a Home Area Network (HAN), by real time monitoring and fine-grained control. However, it offers the possibility for a malicious user to intrude into the HAN and deceive the smart metering system with fraudulent energy usage report. While most of the existing works have focused on how to prevent data tampering in HAN's communication channel, this paper looks into a relatively less studied security aspect namely data provenance. We propose a novel solution based on Shamir's secret sharing and threshold cryptography to guarantee that the reported energy usage is collected from the specific appliance as claimed at a particular location, and that it reflects the real consumption of the energy. A byproduct of the proposed security solution is a guarantee of data integrity. A prototype implementation is presented to demonstrate the feasibility and practicality of the proposed solution

A Lightweight Privacy-Preserved Spatial and Temporal Aggregation of Energy Data

Smart grid provides fine-grained real time energy consumption, and it is able to improve the efficiency of energy management. It enables the collection of energy consumption data from consumer and hence has raised serious privacy concerns. Energy consumption data, a form of personal information that reveals behavioral patterns can be used to identify electrical appliances being used by the user through the electricity load signature, thus making it possible to further reveal the residency pattern of a consumer’s household or appliances usage habit. This paper proposes to enhance the privacy of energy con- sumption data by enabling the utility to retrieve the aggregated spatial and temporal consumption without revealing individual energy consumption. We use a lightweight cryptographic mech- anism to mask the energy consumption data by adding random noises to each energy reading and use Paillier’s additive homo- morphic encryption to protect the noises. When summing up the masked energy consumption data for both Spatial and Temporal aggregation, the noises cancel out each other, hence resulting in either the total sum of energy consumed in a neighbourhood at a particular time, or the total sum of energy consumed by a household in a day. No third party is able to derive the energy consumption pattern of a household in real time. A proof-of- concept was implemented to demonstrate the feasibility of the system, and the results show that the system can be efficiently deployed on a low-cost computing platform

SEABASS: Symmetric-keychain Encryption and Authentication for Building Automation Systems

There is an increasing security risk in Building Automation Systems (BAS) in that its communication is unprotected, resulting in the adversary having the capability to inject spurious commands to the actuators to alter the behaviour of BAS. The communication between the Human-Machine-Interface (HMI) and the controller (PLC) is vulnerable as there is no secret key being used to protect the authenticity, confidentiality and integrity of the sensor data and commands. We propose SEABASS, a lightweight key management scheme to distribute and manage session keys between HMI and PLCs, providing a secure communication channel between any two communicating devices in BAS through a symmetric-key based hash-chain encryption and authentication of message exchange. Our scheme facilitates automatic renewal of session keys periodically based on the use of a reversed hash-chain. A prototype was implemented using the BACnet/IP communication protocol and the preliminary results show that the symmetric keychain approach is lightweight and incurs low latency

Homomorphic Authentication Codes for Network Coding

Authentication codes (A-codes) are a well studied technique to provide unconditionally secure authentication. An A-code is defined by a map that associates a pair formed by a message and a key to a tag. A-codes linear in the keys have been studied for application to distributed authentication schemes. In this paper, we address the dual question, namely the study of A-codes that are linear in the messages. This is usually an undesired property, except in the context of network coding. Regarding these A-codes, we derive some lower bounds on security parameters when key space is known. We also show a lower bound on key size when security parameter values are given (with some special properties) and construct some codes meeting the bound. We finally present a variant of these codes that authenticate multiple messages with a same key while preserving unconditional security

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

A Lightweight Privacy-Preserved Spatial and Temporal Aggregation of Energy Data

Smart grid provides fine-grained real time energy consumption, and it is able to improve the efficiency of energy management. It enables the collection of energy consumption data from consumer and hence has raised serious privacy concerns. Energy consumption data, a form of personal information that reveals behavioral patterns can be used to identify electrical appliances being used by the user through the electricity load signature, thus making it possible to further reveal the residency pattern of a consumer’s household or appliances usage habit. This paper proposes to enhance the privacy of energy con- sumption data by enabling the utility to retrieve the aggregated spatial and temporal consumption without revealing individual energy consumption. We use a lightweight cryptographic mech- anism to mask the energy consumption data by adding random noises to each energy reading and use Paillier’s additive homo- morphic encryption to protect the noises. When summing up the masked energy consumption data for both Spatial and Temporal aggregation, the noises cancel out each other, hence resulting in either the total sum of energy consumed in a neighbourhood at a particular time, or the total sum of energy consumed by a household in a day. No third party is able to derive the energy consumption pattern of a household in real time. A proof-of- concept was implemented to demonstrate the feasibility of the system, and the results show that the system can be efficiently deployed on a low-cost computing platform