We have applied the open-plus-closed-loop control method, recently devised by Grosu et al., to chaos-based communications. In our method, a message is handled as if it were part of a parameter mismatch between the chaotic oscillators installed on a drive and a response system. In the drive system, the message is encrypted by adding it to a state variable of the oscillator as dynamical noise. In the response system, the message is decrypted by subtracting the chaotic signal reproduced by chaotic synchronization using the open-plus-closed-loop control method from the received signal, followed by differentiation with respect to time. When the oscillators have multiple parameter mismatches, multiple messages can be simultaneously encrypted and decrypted to achieve multiplex secure communications.

This paper describes a design of energy-efficient Standard Cell Memory (SCM) using Silicon-on-Thin-BOX (SOTB). We present automatic place and routing (P&R) methodology for optimal body-bias separation (BBS) for SCM, which enables to apply different body bias voltages to latches and to other peripheral circuits within SCM. Capability of SOTB to effectively reduce leakage by body biasing is fully exploited in BBS. Simulation results demonstrated that our approach allows us to design SCM with 40% smaller energy dissipation at the energy minimum voltage as compared to the conventional design flow. For the process and temperature variations, Adaptive Body Bias (ABB) for SCM with our BBS provided 70% smaller leakage energy than ABB for the conventional SCM, while achieving the same clock frequency.

We focus on forward-looking radar network systems for automotive usages. By using multiple radars, the radar network systems will achieve reliable detection and wide observation area. The forward-looking systems by cameras are famous. In order to realize more reliable safety, the cameras had better be used with other sensing devices such as the radar network. In the radar network, processing of the data, which is derived from the multiple receivers, is important because the processing decides the estimation performance. In this paper, we will introduce our estimation algorithm which focuses on target existence probability and virtual receivers. The performance will be evaluated by simulated targets which are both single point model and 3D target model.

Software watermarking enables one to embed some information called “mark” into a program while preserving its functionality, and to read it from the program. As a definition of function preserving, Cohen et al. (STOC 2016) proposed statistical function preserving which requires that the input/output behavior of the marked circuit is identical almost everywhere to that of the original unmarked circuit. They showed how to construct watermarkable cryptographic primitives with statistical function preserving, including pseudorandom functions (PRFs) and public-key encryption from indistinguishability obfuscation. Recently, Goyal et al. (CRYPTO 2019) introduced more relaxed definition of function preserving for watermarkable signature. Watermarkable signature embeds a mark into a signing circuit of digital signature. The relaxed function preserving only requires that the marked signing circuit outputs valid signatures. They provide watermarkable signature with the relaxed function preserving only based on (standard) digital signature. In this work, we introduce an intermediate notion of function preserving for watermarkable signature, which is called computational function preserving. Then, we examine the relationship among our computational function preserving, relaxed function preserving by Goyal et al., and statistical function preserving by Cohen et al. Furthermore, we propose a generic construction of watermarkable signature scheme satisfying computational function preserving based on public key encryption and (standard) digital signature.

Message franking is introduced by Facebook in end-to-end encrypted messaging services. It allows to produce verifiable reports of malicious messages by including cryptographic proofs, called reporting tags, generated by Facebook. Recently, Grubbs et al. (CRYPTO'17) proceeded with the formal study of message franking and introduced committing authenticated encryption with associated data (CAEAD) as a core primitive for obtaining message franking. In this work, we aim to enhance the security of message franking and introduce forward security and updates of reporting tags for message franking. Forward security guarantees the security associated with the past keys even if the current keys are exposed and updates of reporting tags allow for reporting malicious messages after keys are updated. To this end, we firstly propose the notion of key-evolving message franking with updatable reporting tags including additional key and reporting tag update algorithms. Then, we formalize five security requirements: confidentiality, ciphertext integrity, unforgeability, receiver binding, and sender binding. Finally, we show a construction of forward secure message franking with updatable reporting tags based on CAEAD, forward secure pseudorandom generator, and updatable message authentication code.

The learning with errors (LWE) problem is one of the fundamental problems in cryptography and it has many applications in post-quantum cryptography. There are two variants of the problem, the decisional-LWE problem, and the search-LWE problem. LWE search-to-decision reduction shows that the hardness of the search-LWE problem can be reduced to the hardness of the decisional-LWE problem. The efficiency of the reduction can be regarded as the gap in difficulty between the problems. We initiate a study of quantum search-to-decision reduction for the LWE problem and propose a reduction that satisfies sample-preserving. In sample-preserving reduction, it preserves all parameters even the number of instances. Especially, our quantum reduction invokes the distinguisher only 2 times to solve the search-LWE problem, while classical reductions require a polynomial number of invocations. Furthermore, we give a way to amplify the success probability of the reduction algorithm. Our amplified reduction is incomparable to the classical reduction in terms of sample complexity and query complexity. Our reduction algorithm supports a wide class of error distributions and also provides a search-to-decision reduction for the learning parity with noise problem. In the process of constructing the search-to-decision reduction, we give a quantum Goldreich-Levin theorem over ℤq where q is a prime. In short, this theorem states that, if a hardcore predicate a・s (mod q) can be predicted with probability distinctly greater than (1/q) with respect to a uniformly random a ∈ ℤqn, then it is possible to determine s ∈ ℤqn.