# Biblio

Due to the importance of securing electronic transactions, many cryptographic protocols have been employed, that mainly depend on distributed keys between the intended parties. In classical computers, the security of these protocols depends on the mathematical complexity of the encoding functions and on the length of the key. However, the existing classical algorithms 100% breakable with enough computational power, which can be provided by quantum machines. Moving to quantum computation, the field of security shifts into a new area of cryptographic solutions which is now the field of quantum cryptography. The era of quantum computers is at its beginning. There are few practical implementations and evaluations of quantum protocols. Therefore, the paper defines a well-known quantum key distribution protocol which is BB84 then provides a practical implementation of it on IBM QX software. The practical implementations showed that there were differences between BB84 theoretical expected results and the practical implementation results. Due to this, the paper provides a statistical analysis of the experiments by comparing the standard deviation of the results. Using the BB84 protocol the existence of a third-party eavesdropper can be detected. Thus, calculations of the probability of detecting/not detecting a third-party eavesdropping have been provided. These values are again compared to the theoretical expectation. The calculations showed that with the greater number of qubits, the percentage of detecting eavesdropper will be higher.

The problems of random numbers application to the information security of data, communication lines, computer units and automated driving systems are considered. The possibilities for making up quantum generators of random numbers and existing solutions for acquiring of sufficiently random sequences are analyzed. The authors found out the method for the creation of quantum generators on the basis of semiconductor electronic components. The electron-quantum generator based on electrons tunneling is experimentally demonstrated. It is shown that it is able to create random sequences of high security level and satisfying known NIST statistical tests (P-Value\textbackslashtextgreater0.9). The generator created can be used for formation of both closed and open cryptographic keys in computer systems and other platforms and has great potential for realization of random walks and probabilistic computing on the basis of neural nets and other IT problems.

In this work, a quantum design for the Simplified-Advanced Encryption Standard (S-AES) algorithm is presented. Also, a quantum Grover attack is modeled on the proposed quantum S-AES. First, quantum circuits for the main components of S-AES in the finite field F2[x]/(x4 + x + 1), are constructed. Then, the constructed circuits are put together to form a quantum version of S-AES. A C-NOT synthesis is used to decompose some of the functions to reduce the number of the needed qubits. The quantum S-AES is integrated into a black-box queried by Grover's algorithm. A new approach is proposed to uniquely recover the secret key when Grover attack is applied. The entire work is simulated and tested on a quantum mechanics simulator. The complexity analysis shows that a block cipher can be designed as a quantum circuit with a polynomial cost. In addition, the secret key is recovered in quadratic speedup as promised by Grover's algorithm.

Securing Internet of things is a major concern as it deals with data that are personal, needed to be reliable, can direct and manipulate device decisions in a harmful way. Also regarding data generation process is heterogeneous, data being immense in volume, complex management. Quantum Computing and Internet of Things (IoT) coined as Quantum IoT defines a concept of greater security design which harness the virtue of quantum mechanics laws in Internet of Things (IoT) security management. Also it ensures secured data storage, processing, communication, data dynamics. In this paper, an IoT security infrastructure is introduced which is a hybrid one, with an extra layer, which ensures quantum state. This state prevents any sort of harmful actions from the eavesdroppers in the communication channel and cyber side, by maintaining its state, protecting the key by quantum cryptography BB84 protocol. An adapted version is introduced specific to this IoT scenario. A classical cryptography system `One-Time pad (OTP)' is used in the hybrid management. The novelty of this paper lies with the integration of classical and quantum communication for Internet of Things (IoT) security.

Quantum low probability of intercept transmits ciphertext in a way that prevents an eavesdropper possessing the decryption key from recovering the plaintext. It is capable of Gbps communication rates on optical fiber over metropolitan-area distances.

Consider a thin, flexible wire of fixed length that is held at each end by a robotic gripper. Any curve traced by this wire when in static equilibrium is a local solution to a geometric optimal control problem, with boundary conditions that vary with the position and orientation of each gripper. We prove that the set of all local solutions to this problem over all possible boundary conditions is a smooth manifold of finite dimension that can be parameterized by a single chart. We show that this chart makes it easy to implement a sampling-based algorithm for quasi-static manipulation planning. We characterize the performance of such an algorithm with experiments in simulation.

Consider a thin, flexible wire of fixed length that is held at each end by a robotic gripper. Any curve traced by this wire when in static equilibrium is a local solution to a geometric optimal control problem, with boundary conditions that vary with the position and orientation of each gripper. We prove that the set of all local solutions to this problem over all possible boundary conditions is a smooth manifold of finite dimension that can be parameterized by a single chart. We show that this chart makes it easy to implement a sampling-based algorithm for quasi-static manipulation planning. We characterize the performance of such an algorithm with experiments in simulation.

Published in The International Journal of Robotics Research

Quasi-steady-state (QSS) large-signal models are often taken for granted in the analysis and design of DC-DC switching converters, particularly for varying operating conditions. In this study, the premise for the QSS is justified quantitatively for the first time. Based on the QSS, the DC-DC switching converter under varying operating conditions is reduced to the linear time varying systems model. Thereafter, the QSS concept is applied to analysis of frequency-domain properties of the DC-DC switching converters by using three-dimensional Bode plots, which is then utilised to the optimisation of the controller parameters for wide variations of input voltage and load resistance. An experimental prototype of an average-current-mode-controlled boost DC-DC converter is built to verify the analysis and design by both frequency-domain and time-domain measurements.

Quality of service (QoS) has been considered as a significant criterion for querying among functionally similar web services. Most researches focus on the search of QoS under certain data which may not cover some practical scenarios. Recent approaches for uncertain QoS of web service deal with discrete data domain. In this paper, we try to build the search of QoS under continuous probability distribution. We offer the definition of two kinds of queries under uncertain QoS and form the optimization approaches for specific distributions. Based on that, the search is extended to general cases. With experiments, we show the feasibility of the proposed methods.

When transferring sensitive data to a non-trusted party, end-users require that the data be kept private. Mobile and IoT application developers want to leverage the sensitive data to provide better user experience and intelligent services. Unfortunately, existing programming abstractions make it impossible to reconcile these two seemingly conflicting objectives. In this paper, we present a novel programming mechanism for distributed managed execution environments that hides sensitive user data, while enabling developers to build powerful and intelligent applications, driven by the properties of the sensitive data. Specifically, the sensitive data is never revealed to clients, being protected by the runtime system. Our abstractions provide declarative and configurable data query interfaces, enforced by a lightweight distributed runtime system. Developers define when and how clients can query the sensitive data's properties (i.e., how long the data remains accessible, how many times its properties can be queried, which data query methods apply, etc.). Based on our evaluation, we argue that integrating our novel mechanism with the Java Virtual Machine (JVM) can address some of the most pertinent privacy problems of IoT and mobile applications.

Originally implemented by Google, QUIC gathers a growing interest by providing, on top of UDP, the same service as the classical TCP/TLS/HTTP/2 stack. The IETF will finalise the QUIC specification in 2019. A key feature of QUIC is that almost all its packets, including most of its headers, are fully encrypted. This prevents eavesdropping and interferences caused by middleboxes. Thanks to this feature and its clean design, QUIC is easier to extend than TCP. In this paper, we revisit the reliable transmission mechanisms that are included in QUIC. More specifically, we design, implement and evaluate Forward Erasure Correction (FEC) extensions to QUIC. These extensions are mainly intended for high-delays and lossy communications such as In-Flight Communications. Our design includes a generic FEC frame and our implementation supports the XOR, Reed-Solomon and Convolutional RLC error-correcting codes. We also conservatively avoid hindering the loss-based congestion signal by distinguishing the packets that have been received from the packets that have been recovered by the FEC. We evaluate its performance by applying an experimental design covering a wide range of delay and packet loss conditions with reproducible experiments. These confirm that our modular design allows the protocol to adapt to the network conditions. For long data transfers or when the loss rate and delay are small, the FEC overhead negatively impacts the download completion time. However, with high packet loss rates and long delays or smaller files, FEC allows drastically reducing the download completion time by avoiding costly retransmission timeouts. These results show that there is a need to use FEC adaptively to the network conditions.

With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.

Quickest detection of false data injection attacks (FDIAs) in dynamic smart grids is considered in this paper. The unknown time-varying state variables of the smart grid and the FDIAs impose a significant challenge for designing a computationally efficient detector. To address this challenge, we propose new Cumulative-Sum-type algorithms with computational complex scaling linearly with the number of meters. Moreover, for any constraint on the expected false alarm period, a lower bound on the threshold employed in the proposed algorithm is provided. For any given threshold employed in the proposed algorithm, an upper bound on the worstcase expected detection delay is also derived. The proposed algorithm is numerically investigated in the context of an IEEE standard power system under FDIAs, and is shown to outperform some representative algorithm in the test case.

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