Roadmap on optical security

Postprint (author's final draft

FPGA based technical solutions for high throughput data processing and encryption for 5G communication: A review

The field programmable gate array (FPGA) devices are ideal solutions for high-speed processing applications, given their flexibility, parallel processing capability, and power efficiency. In this review paper, at first, an overview of the key applications of FPGA-based platforms in 5G networks/systems is presented, exploiting the improved performances offered by such devices. FPGA-based implementations of cloud radio access network (C-RAN) accelerators, network function virtualization (NFV)-based network slicers, cognitive radio systems, and multiple input multiple output (MIMO) channel characterizers are the main considered applications that can benefit from the high processing rate, power efficiency and flexibility of FPGAs. Furthermore, the implementations of encryption/decryption algorithms by employing the Xilinx Zynq Ultrascale+MPSoC ZCU102 FPGA platform are discussed, and then we introduce our high-speed and lightweight implementation of the well-known AES-128 algorithm, developed on the same FPGA platform, and comparing it with similar solutions already published in the literature. The comparison results indicate that our AES-128 implementation enables efficient hardware usage for a given data-rate (up to 28.16 Gbit/s), resulting in higher efficiency (8.64 Mbps/slice) than other considered solutions. Finally, the applications of the ZCU102 platform for high-speed processing are explored, such as image and signal processing, visual recognition, and hardware resource management

SecureAD: A secure video anomaly detection framework on convolutional neural network in edge computing environment

National Research Foundation (NRF) Singapore under Strategic Capability Research Centres Funding Intiatives; Ministry of Education, Singapore under its Academic Research Funding Tier

Geo-tagging and privacy-preservation in mobile cloud computing

With the emerge of the cloud computing service and the explosive growth of the mobile devices and applications, mobile computing technologies and cloud computing technologies have been drawing significant attentions. Mobile cloud computing, with the synergy between the cloud and mobile technologies, has brought us new opportunities to develop novel and practical systems such as mobile multimedia systems and cloud systems that provide collaborative data-mining services for data from disparate owners (e.g., mobile users). However, it also creates new challenges, e.g., the algorithms deployed in the computationally weak mobile device require higher efficiency, and introduces new problems such as the privacy concern when the private data is shared in the cloud for collaborative data-mining. The main objectives of this dissertation are: 1. to develop practical systems based on the unique features of mobile devices (i.e., all-in-one computing platform and sensors) and the powerful computing capability of the cloud; 2. to propose solutions protecting the data privacy when the data from disparate owners are shared in the cloud for collaborative data-mining. We first propose a mobile geo-tagging system. It is a novel, accurate and efficient image and video based remote target localization and tracking system using the Android smartphone. To cope with the smartphones' computational limitation, we design light-weight image/video processing algorithms to achieve a good balance between estimation accuracy and computational complexity. Our system is first of its kind and we provide first hand real-world experimental results, which demonstrate that our system is feasible and practicable. To address the privacy concern when data from disparate owners are shared in the cloud for collaborative data-mining, we then propose a generic compressive sensing (CS) based secure multiparty computation (MPC) framework for privacy-preserving collaborative data-mining in which data mining is performed in the CS domain. We perform the CS transformation and reconstruction processes with MPC protocols. We modify the original orthogonal matching pursuit algorithm and develop new MPC protocols so that the CS reconstruction process can be implemented using MPC. Our analysis and experimental results show that our generic framework is capable of enabling privacy preserving collaborative data-mining. The proposed framework can be applied to many privacy preserving collaborative data-mining and signal processing applications in the cloud. We identify an application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We further propose a privacy preserving storage and secure watermark detection framework by adopting our generic framework to address such a requirement. In our secure watermark detection framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a compressive sensing domain to protect the privacy. We also give mathematical and statistical analysis to derive the expected watermark detection performance in the compressive sensing domain, based on the target image, watermark pattern and the size of the compressive sensing matrix (but without the actual CS matrix), which means that the watermark detection performance in the CS domain can be estimated during the watermark embedding process. The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the compressive sensing domain is feasible. By taking advantage of our mobile geo-tagging system and compressive sensing based privacy preserving data-mining framework, we develop a mobile privacy preserving collaborative filtering system. In our system, mobile users can share their personal data with each other in the cloud and get daily activity recommendations based on the data-mining results generated by the cloud, without leaking the privacy and secrecy of the data to other parties. Experimental results demonstrate that the proposed system is effective in enabling efficient mobile privacy preserving collaborative filtering services.Includes bibliographical references (pages 126-133)

GME: GPU-based Microarchitectural Extensions to Accelerate Homomorphic Encryption

Fully Homomorphic Encryption (FHE) enables the processing of encrypted data without decrypting it. FHE has garnered significant attention over the past decade as it supports secure outsourcing of data processing to remote cloud services. Despite its promise of strong data privacy and security guarantees, FHE introduces a slowdown of up to five orders of magnitude as compared to the same computation using plaintext data. This overhead is presently a major barrier to the commercial adoption of FHE. In this work, we leverage GPUs to accelerate FHE, capitalizing on a well-established GPU ecosystem available in the cloud. We propose GME, which combines three key microarchitectural extensions along with a compile-time optimization to the current AMD CDNA GPU architecture. First, GME integrates a lightweight on-chip compute unit (CU)-side hierarchical interconnect to retain ciphertext in cache across FHE kernels, thus eliminating redundant memory transactions. Second, to tackle compute bottlenecks, GME introduces special MOD-units that provide native custom hardware support for modular reduction operations, one of the most commonly executed sets of operations in FHE. Third, by integrating the MOD-unit with our novel pipelined $64$-bit integer arithmetic cores (WMAC-units), GME further accelerates FHE workloads by $19\%$. Finally, we propose a Locality-Aware Block Scheduler (LABS) that exploits the temporal locality available in FHE primitive blocks. Incorporating these microarchitectural features and compiler optimizations, we create a synergistic approach achieving average speedups of $796\times$, $14.2\times$, and $2.3\times$ over Intel Xeon CPU, NVIDIA V100 GPU, and Xilinx FPGA implementations, respectively

Roadmap on optical security

Information security and authentication are important challenges facing society. Recent attacks by hackers on the databases of large commercial and financial companies have demonstrated that more research and development of advanced approaches are necessary to deny unauthorized access to critical data. Free space optical technology has been investigated by many researchers in information security, encryption, and authentication. The main motivation for using optics and photonics for information security is that optical waveforms possess many complex degrees of freedom such as amplitude, phase, polarization, large bandwidth, nonlinear transformations, quantum properties of photons, and multiplexing that can be combined in many ways to make information encryption more secure and more difficult to attack. This roadmap article presents an overview of the potential, recent advances, and challenges of optical security and encryption using free space optics. The roadmap on optical security is comprised of six categories that together include 16 short sections written by authors who have made relevant contributions in this field. The first category of this roadmap describes novel encryption approaches, including secure optical sensing which summarizes double random phase encryption applications and flaws [Yamaguchi], the digital holographic encryption in free space optical technique which describes encryption using multidimensional digital holography [Nomura], simultaneous encryption of multiple signals [Pérez-Cabré], asymmetric methods based on information truncation [Nishchal], and dynamic encryption of video sequences [Torroba]. Asymmetric and one-way cryptosystems are analyzed by Peng. The second category is on compression for encryption. In their respective contributions, Alfalou and Stern propose similar goals involving compressed data and compressive sensing encryption. The very important area of cryptanalysis is the topic of the third category with two sections: Sheridan reviews phase retrieval algorithms to perform different attacks, whereas Situ discusses nonlinear optical encryption techniques and the development of a rigorous optical information security theory. The fourth category with two contributions reports how encryption could be implemented at the nano- or micro-scale. Naruse discusses the use of nanostructures in security applications and Carnicer proposes encoding information in a tightly focused beam. In the fifth category, encryption based on ghost imaging using single-pixel detectors is also considered. In particular, the authors [Chen, Tajahuerce] emphasize the need for more specialized hardware and image processing algorithms. Finally, in the sixth category, Mosk and Javidi analyze in their corresponding papers how quantum imaging can benefit optical encryption systems. Sources that use few photons make encryption systems much more difficult to attack, providing a secure method for authentication.Centro de Investigaciones ÓpticasConsejo Nacional de Investigaciones Científicas y Técnica

Data Encryption and Hashing Schemes for Multimedia Protection

There are millions of people using social networking sites like Facebook, Google+, and Youtube every single day across the entire world for sharing photos and other digital media. Unfortunately, sometimes people publish content that does not belong to them. As a result, there is an increasing demand for quality software capable of providing maximum protection for copyrighted material. In addition, confidential content such as medical images and patient records require high level of security so that they can be protected from unintended disclosure, when transferred over the Internet. On the other hand, decreasing the size of an image without significant loss in quality is always highly desirable. Hence, the need for efficient compression algorithms. This thesis introduces a robust method for image compression in the shearlet domain. Motivated by the outperformance of the Discrete Shearlet Transform (DST) compared to the Discrete Wavelet Transform (DWT) in encoding the directional information in images, we propose a DST-based compression algorithm that provides not only a better quality in terms of image approximation and compression ratio, but also increases the security of images via the Advanced Encryption Standard. Experimental results on a slew of medical images illustrate an improved performance in image quality of the proposed approximation approach in comparison to DWT, and also demonstrate its robustness against a variety of tests, including randomness, entropy, key sensitivity, and input sensitivity. We also present a 3D mesh hashing technique using spectral graph theory. The main idea is to partition a 3D model into sub-meshes, followed by the generation of the Laplace-Beltrami matrix of each sub-mesh, and the application of eigen-decomposition. This, in turn, is followed by the hashing of each sub-mesh using Tsallis entropy. The experimental results using a benchmark 3D models demonstrate the effectiveness of the proposed hashing scheme

Parallel Multiplier Designs for the Galois/Counter Mode of Operation

The Galois/Counter Mode of Operation (GCM), recently standardized by NIST, simultaneously authenticates and encrypts data at speeds not previously possible for both software and hardware implementations. In GCM, data integrity is achieved by chaining Galois field multiplication operations while a symmetric key block cipher such as the Advanced Encryption Standard (AES), is used to meet goals of confidentiality. Area optimization in a number of proposed high throughput GCM designs have been approached through implementing efficient composite Sboxes for AES. Not as much work has been done in reducing area requirements of the Galois multiplication operation in the GCM which consists of up to 30% of the overall area using a bruteforce approach. Current pipelined implementations of GCM also have large key change latencies which potentially reduce the average throughput expected under traditional internet traffic conditions. This thesis aims to address these issues by presenting area efficient parallel multiplier designs for the GCM and provide an approach for achieving low latency key changes. The widely known Karatsuba parallel multiplier (KA) and the recently proposed Fan-Hasan multiplier (FH) were designed for the GCM and implemented on ASIC and FPGA architectures. This is the first time these multipliers have been compared with a practical implementation, and the FH multiplier showed note worthy improvements over the KA multiplier in terms of delay with similar area requirements. Using the composite Sbox, ASIC designs of GCM implemented with subquadratic multipliers are shown to have an area savings of up to 18%, without affecting the throughput, against designs using the brute force Mastrovito multiplier. For low delay LUT Sbox designs in GCM, although the subquadratic multipliers are a part of the critical path, implementations with the FH multiplier showed the highest efficiency in terms of area resources and throughput over all other designs. FPGA results similarly showed a significant reduction in the number of slices using subquadratic multipliers, and the highest throughput to date for FPGA implementations of GCM was also achieved. The proposed reduced latency key change design, which supports all key types of AES, showed a 20% improvement in average throughput over other GCM designs that do not use the same techniques. The GCM implementations provided in this thesis provide some of the most area efficient, yet high throughput designs to date