Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one.

Nowadays, we are surrounded by devices collecting and transmitting private information. Currently, the two main mathematical problems that guarantee security on the Internet are the Integer Factorization Problem and the Discrete Logarithm Problem. However, Shor’s quantum algorithm can easily solve both problems. Therefore, research into cryptographic algorithms that run in classical computers and are resistant to quantum computers is extremely necessary. This area is known as post-quantum cryptography and usually studies asymmetric cryptography. By means of asymptotic analysis, the purpose of this paper is to provide an evaluation of security and its performance for the types of cryptographic systems considered safe against quantum attacks in the second-round NIST Post-Quantum Standardization Process, namely isogeny cryptosystems based on supersingular elliptic curves, error correction code-based encryption system, and lattice-based ring learning with errors. We performed a security comparison of Key Agreements protocols based on these three post-quantum cryptographic primitives and compared them with Discrete Logarithm Problem and Integer Factorization Problem. The comparison of security and its performance is presented by security level, the former by complexity analyses to achieve theoretical minimum key sizes, and the latter by simulation to assess a practical performance comparison. In the complexity analysis, as we increase the security level and then the size of the cryptographic keys increases, techniques based on isogeny outperform all other post-quantum algorithms in relation to key sizes at practical security level. In the performance comparison, the results show that the code-based protocol presents the best results among the others.

The main contribution of this paper is the construction of the efficient privacy-preserving protocol for smart metering systems (EPPP4SMS), which brings together features of the best privacy-preserving protocols in the literature for smart grids. In addition, EPPP4SMS is faster on the meter side—and in the whole round (encryption, aggregation, and decryption)—than other protocols based on homomorphic encryption and it is still scalable. Moreover, EPPP4SMS enables energy suppliers and customers to verify the billing information and measurements without leaking private information. Since the energy supplier knows the amount of generated electricity and its flow throughout electrical substations, the energy supplier can use this verification to detect energy loss and fraud. Different from verification based on digital signature, our verification enables new features; for example, smart meters and their energy supplier can compute the verification without storing the respective encrypted measurements. Furthermore, EPPP4SMS may be suitable to many other scenarios that need aggregation of time-series data keeping privacy protected, including electronic voting, reputation systems, and sensor networks. In this paper, we present theoretical results of EPPP4SMS and their validation by implementation of algorithms and simulation using real-world measurement data.

We present an innovative protocol combining innetwork data aggregation and smart meter billing for a smart grid scenario. The former enables an energy supplier to allocate and balance resources. The latter provides dynamic pricing schemes according to fine-grained consumption profiles. Moreover, smart meters and their energy supplier can prove their billing values. Since the energy supplier knows the amount of generated electricity and the consolidated consumption in a round of measurements, the energy supplier can detect energy loss and fraud. To preserve customers' privacy, we use a homomorphic commitment scheme with a homomorphic encryption scheme. All data sent from a meter to any other component in the communication network is either a commitment or an encrypted message. To provide security and privacy, we only require software modifications, leaving the hardware of the smart grid unchanged.