Privacy-Preserving Data Falsification Detection in Smart Grids using Elliptic Curve Cryptography and Homomorphic Encryption

In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers\u27 fine-grained power consumption data; however, analyzing customers\u27 private data violates the customers\u27 privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt elliptic curve cryptography (ECC) based on homomorphic encryption (HE) without revealing customer power consumption data. HE is a form of encryption that permits users to perform computations on the encrypted data without decryption. Through ECC, we can achieve light computation. Our experimental evaluation showed that our proposed scheme successfully achieved 18 times faster than the CKKS scheme, a common HE scheme

Secure Computation over Lattices and Elliptic Curves

Traditional threshold cryptosystems have decentralized core cryptographic primitives like key generation, decryption and signatures. Most threshold cryptosystems, however, rely on special purpose protocols that cannot easily be integrated into more complex multiparty protocols. In this work, we design and implement decentralized versions of lattice-based and elliptic-curve-based public-key cryptoystems using generic secure multiparty computation (MPC) protocols. These are standard cryptosystems, so we introduce no additional work for encrypting devices and no new assumptions beyond those of the generic MPC framework. Both cryptosystems are also additively homomorphic, which allows for secure additions directly on ciphertexts. By using generic MPC techniques, our multiparty decryption protocols compute secret-shares of the plaintext, whereas most special-purpose cryptosystems either do not support decryption or must reveal the decryptions in the clear. Our method allows complex functions to be securely evaluated after decryption, revealing only the results of the functions and not the plaintexts themselves. To improve performance, we present a novel oblivious elliptic curve multiplication protocol and a new noise-masking technique which may be of independent interest. We implemented our protocols using the SCALE-MAMBA secure multiparty computation platform, which provides security against malicious adversaries and supports arbitrary numbers of participants

Encrypted control for networked systems -- An illustrative introduction and current challenges

Cloud computing and distributed computing are becoming ubiquitous in many modern control systems such as smart grids, building automation, robot swarms or intelligent transportation systems. Compared to "isolated" control systems, the advantages of cloud-based and distributed control systems are, in particular, resource pooling and outsourcing, rapid scalability, and high performance. However, these capabilities do not come without risks. In fact, the involved communication and processing of sensitive data via public networks and on third-party platforms promote, among other cyberthreats, eavesdropping and manipulation of data. Encrypted control addresses this security gap and provides confidentiality of the processed data in the entire control loop. This paper presents a tutorial-style introduction to this young but emerging field in the framework of secure control for networked dynamical systems.Comment: The paper is a preprint of an accepted paper in the IEEE Control Systems Magazin

A New Framework for Privacy-Preserving Aggregation of Time-Series Data

International audienceAggregator-oblivious encryption is a useful notion put forward by Shi et al. in 2011 that allows an untrusted aggregator to periodically compute an aggregate value over encrypted data contributed by a set of users. Such encryption schemes find numerous applications, in particular in the context of privacy-preserving smart metering.This paper presents a general framework for constructing privacy-preserving aggregator-oblivious encryption schemes using a variant of Cramer-Shoup's paradigm of smooth projective hashing. This abstraction leads to new schemes based on a variety of complexity assumptions. It also improves upon existing constructions, providing schemes with shorter ciphertexts and better encryption times

Scalable and Secure Aggregation in Distributed Networks

We consider the problem of computing an aggregation function in a \emph{secure} and \emph{scalable} way. Whereas previous distributed solutions with similar security guarantees have a communication cost of $O(n^3)$, we present a distributed protocol that requires only a communication complexity of $O(n\log^3 n)$, which we prove is near-optimal. Our protocol ensures perfect security against a computationally-bounded adversary, tolerates $(1/2-\epsilon)n$ malicious nodes for any constant $1/2 > \epsilon > 0$ (not depending on $n$), and outputs the exact value of the aggregated function with high probability