Low Storage Complexity Research Articles

Learning-Based High-Frame-Rate SAR Imaging

As high-frame-rate synthetic aperture radar (SAR) has the ability to form continuous SAR images and dynamically monitor the ground areas of interest, it has attracted more and more attention nowadays. In practical application, the enormous data in high-frame-rate SAR system to obtain the multi-frame images brings big challenges to its transmission, storage, and processing. In order to solve this problem, there are many recent papers on formulating the high-frame-rate SAR imaging problem into a low-rank tensor recovery problem, and correspondingly, the sampling amount of the high-frame-rate SAR data can be largely reduced. However, existing algorithms to solve the low-rank tensor recovery problem in high-frame-rate SAR application still suffer from large computational cost. Under the above inspiration, this paper proposes a deep neural network architecture for high-frame-rate SAR imaging, i.e., Tensor Alternating Direction Method of Multiplier Network (TADMM-Net), which is more computational efficient in imaging procedure. Specifically, we formulate the high-frame-rate SAR imaging processing into a low-tubal-rank tensor recovery problem. We solve the low-tubal-rank tensor recovery problem using a tensor Alternating Direction Method of Multiplier (ADMM) algorithm and then design a new deep neural network architecture by applying algorithm unfolding techniques to the underlying low-rank tensor recovery problem. The proposed TADMM-Net approach shifts the computational burden from the testing phase to the training phase, and the practical processing time can be extremely decreased compared with the existing algorithms for the low-rank tensor recovery problem in high-frame-rate SAR imaging application. It also offers various advantages over the existing high-frame-rate SAR imaging algorithms including higher performance in the case with low sampling amount, lower storage complexity and no requirement for hand-craft hyper-parameters adjustment. The methodology was tested on high-frame-rate SAR data. These tests show that the proposed architecture outperforms other state-of-the-art methods in high-frame-rate SAR imaging applications.

Lightweight Secure Integer Comparison

We solve the millionaires problem in the semi-trusted model with homomorphic encryption without using intermediate decryptions. This leads to the computationally least expensive solution with homomorphic encryption so far, with a low bandwidth and very low storage complexity. The number of modular multiplications needed is less than the number of modular multiplications needed for one Pallier encryption. The output of the protocol can be either publicly known, encrypted, or secret-shared. The private input of the first player is computationally secure towards the second player, and the private input of the second player is even unconditionally secure towards the first player. We also introduce an efficient client-server solution for the millionaires problem with similar security properties.

Secure and Efficient Multi-Signature Schemes for Fabric: An Enterprise Blockchain Platform

Digital signature is a major component of transactions on Blockchain platforms, especially in enterprise Blockchain platforms, where multiple signatures from a set of peers need to be produced to endorse a transaction. However, such process is often complex and time-consuming. Multi-signature, which can improve transaction efficiency by having a set of signers cooperate to produce a joint signature, has attracted extensive attentions. In this work, we propose two multi-signature schemes, GMS and AGMS, which are proved to be more secure and efficient than state-of-the-art multi-signature schemes. Besides, we implement the proposed schemes in a real Enterprise Blockchain platform, Fabric. Experiment results show that the proposed AGMS scheme helps achieve the goal of high transaction efficiency, low storage complexity, as well as high robustness against rogue-key attacks and $k$ -sum problem attacks.

An incremental approach for data quality measurement with insufficient information

Recently, a fundamental study on measurement of data quality introduced an ordinal-scaled procedure of measurement. Besides the pure ordinal information about the level of quality, numerical information is induced when considering uncertainty involved during measurement. In the case where uncertainty is modelled as probability, this numerical information is ratio-scaled. An essential property of the mentioned approach is that the application of a measure on a large collection of data can be represented efficiently in the sense that (i) the representation has a low storage complexity and (ii) it can be updated incrementally when new data are observed. However, this property only holds when the evaluation of predicates is clear and does not deal with uncertainty. For some dimensions of quality, this assumption is far too strong and uncertainty comes into play almost naturally. In this paper, we investigate how the presence of uncertainty influences the efficiency of a measurement procedure. Hereby, we focus specifically on the case where uncertainty is caused by insufficient information and is thus modelled by means of possibility theory. It is shown that the amount of data that reaches a certain level of quality, can be summarized as a possibility distribution over the set of natural numbers. We investigate an approximation of this distribution that has a controllable loss of information, allows for incremental updates and exhibits a low space complexity.

Boolean permutation-based key escrow

There has been an increasing interest in the design and use of key escrow schemes in recent times. This paper proposes a new key escrow protocol based on Boolean permutations and analyses its properties. It shows that the verification of the shares of key escrow agencies is easy and secure. In addition this protocol provides forward security as well as the capability to fully disclose the escrowed private keys in the case of guilty users. The paper also proposes methods for constructing practical trapdoor Boolean permutations with a low storage complexity. Apart from the proposal of the key escrow protocol itself, this paper describes some of the key properties of Boolean permutations which make them suitable for their use in the design of cryptosystems.