Review on Leakage Resilient Key Exchange Security Model

In leakage resilient cryptography, leakage resilient key exchange protocols are constructed to defend against leakage attacks. Then, the key exchange protocol is proved with leakage resilient security model to determine whether its security proof can provide the security properties it claimed or to find out any unexamined flaw during protocol building. It is an interesting work to review the meaningful security properties provided by these security models. This work review how a leakage resilient security model for a key exchange protocol has been evolved over years according to the increasing security requirement which covers a different range of attacks. The relationship on how an adversary capability in the leakage resilient security model can be related to real-world attack scenarios is studied. The analysis work for each leakage resilient security model here enables a better knowledge on how an adversary query addresses different leakage attacks setting, thereby understand the motive of design for a cryptographic primitive in the security model

Locally Decodable and Updatable Non-Malleable Codes in the Bounded Retrieval Model

In a recent result, Dachman-Soled et al.(TCC \u2715) proposed a new notion called locally decodable and updatable non-malleable codes, which informally, provides the security guarantees of a non-malleable code while also allowing for efficient random access. They also considered locally decodable and updatable non-malleable codes that are leakage-resilient, allowing for adversaries who continually leak information in addition to tampering. The bounded retrieval model (BRM) (cf. [Alwen et al., CRYPTO \u2709] and [Alwen et al., EUROCRYPT \u2710]) has been studied extensively in the setting of leakage resilience for cryptographic primitives. This threat model assumes that an attacker can learn information about the secret key, subject only to the constraint that the overall amount of leaked information is upper bounded by some value. The goal is then to construct cryptosystems whose secret key length grows with the amount of leakage, but whose runtime (assuming random access to the secret key) is independent of the leakage amount. In this work, we combine the above two notions and construct locally decodable and updatable non-malleable codes in the split-state model, that are secure against bounded retrieval adversaries. Specifically, given leakage parameter l, we show how to construct an efficient, 3-split-state, locally decodable and updatable code (with CRS) that is secure against one-time leakage of any polynomial time, 3-split-state leakage function whose output length is at most l, and one-time tampering via any polynomial-time 3-split-state tampering function. The locality we achieve is polylogarithmic in the security parameter

Cryptographic techniques for hardware security

Traditionally, cryptographic algorithms are designed under the so-called black-box model, which considers adversaries that receive black-box access to the hardware implementation. Although a "black-box" treatment covers a wide range of attacks, it fails to capture reality adequately, as real-world adversaries can exploit physical properties of the implementation, mounting attacks that enable unexpected, non-black-box access, to the components of the cryptographic system. This type of attacks is widely known as physical attacks, and has proven to be a significant threat to the real-world security of cryptographic systems. The present dissertation is (partially) dealing with the problem of protecting cryptographic memory against physical attacks, via the use of non-malleable codes, which is a notion introduced in a preceding work, aiming to provide privacy of the encoded data, in the presence of adversarial faults. In the present thesis we improve the current state-of-the-art on non-malleable codes and we provide practical solutions for protecting real-world cryptographic implementations against physical attacks. Our study is primarily focusing on the following adversarial models: (i) the extensively studied split-state model, which assumes that private memory splits into two parts, and the adversary tampers with each part, independently, and (ii) the model of partial functions, which is introduced by the current thesis, and models adversaries that access arbitrary subsets of codeword locations, with bounded cardinality. Our study is comprehensive, covering one-time and continuous, attacks, while for the case of partial functions, we manage to achieve a stronger notion of security, that we call non-malleability with manipulation detection, that in addition to privacy, it also guarantees integrity of the private data. It should be noted that, our techniques are also useful for the problem of establishing, private, keyless communication, over adversarial communication channels. Besides physical attacks, another important concern related to cryptographic hardware security, is that the hardware fabrication process is assumed to be trusted. In reality though, when aiming to minimize the production costs, or whenever access to leading-edge manufacturing facilities is required, the fabrication process requires the involvement of several, potentially malicious, facilities. Consequently, cryptographic hardware is susceptible to the so-called hardware Trojans, which are hardware components that are maliciously implanted to the original circuitry, having as a purpose to alter the device's functionality, while remaining undetected. Part of the present dissertation, deals with the problem of protecting cryptographic hardware against Trojan injection attacks, by (i) proposing a formal model for assessing the security of cryptographic hardware, whose production has been partially outsourced to a set of untrusted, and possibly malicious, manufacturers, and (ii) by proposing a compiler that transforms any cryptographic circuit, into another, that can be securely outsourced

Secure Data Sharing in Cloud Computing: A Comprehensive Review

Cloud Computing is an emerging technology, which relies on sharing computing resources. Sharing of data in the group is not secure as the cloud provider cannot be trusted. The fundamental difficulties in distributed computing of cloud suppliers is Data Security, Sharing, Resource scheduling and Energy consumption. Key-Aggregate cryptosystem used to secure private/public data in the cloud. This key is consistent size aggregate for adaptable decisions of ciphertext in cloud storage. Virtual Machines (VMs) provisioning is effectively empowered the cloud suppliers to effectively use their accessible resources and get higher benefits. The most effective method to share information resources among the individuals from the group in distributed storage is secure, flexible and efficient. Any data stored in different cloud data centers are corrupted, recovery using regenerative coding. Security is provided many techniques like Forward security, backward security, Key-Aggregate cryptosystem, Encryption and Re-encryption etc. The energy is reduced using Energy-Efficient Virtual Machines Scheduling in Multi-Tenant Data Centers

Secure data sharing in cloud computing: a comprehensive review

Cloud Computing is an emerging technology, which relies on sharing computing resources. Sharing of data in the group is not secure as the cloud provider cannot be trusted. The fundamental difficulties in distributed computing of cloud suppliers is Data Security, Sharing, Resource scheduling and Energy consumption. Key-Aggregate cryptosystem used to secure private/public data in the cloud. This key is consistent size aggregate for adaptable decisions of ciphertext in cloud storage. Virtual Machines (VMs) provisioning is effectively empowered the cloud suppliers to effectively use their accessible resources and get higher benefits. The most effective method to share information resources among the individuals from the group in distributed storage is secure, flexible and efficient. Any data stored in different cloud data centers are corrupted, recovery using regenerative coding. Security is provided many techniques like Forward security, backward security, Key-Aggregate cryptosystem, Encryption and Re-encryption etc. The energy is reduced using Energy-Efficient Virtual Machines Scheduling in Multi-Tenant Data Centers

Federated Learning in Computer Vision

Federated Learning (FL) has recently emerged as a novel machine learning paradigm allowing to preserve privacy and to account for the distributed nature of the learning process in many real-world settings. Computer vision tasks deal with huge datasets often with critical privacy issues, therefore many federated learning approaches have been presented to exploit its distributed and privacy-preserving nature. Firstly, this paper introduces the different FL settings used in computer vision and the main challenges that need to be tackled. Then, it provides a comprehensive overview of the different strategies used for FL in vision applications and presents several different approaches for image classification, object detection, semantic segmentation and for focused settings in face recognition and medical imaging. For the various approaches the considered FL setting, the employed data and methodologies and the achieved results are thoroughly discussed

Software Protection and Secure Authentication for Autonomous Vehicular Cloud Computing

Artificial Intelligence (AI) is changing every technology we deal with. Autonomy has been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected coined as Autonomous Vehicles (AVs). Moreover, researchers found a way to make more use of these enormous capabilities and introduced Autonomous Vehicles Cloud Computing (AVCC). In these platforms, vehicles can lend their unused resources and sensory data to join AVCC. In this dissertation, we investigate security and privacy issues in AVCC. As background, we built our vision of a layer-based approach to thoroughly study state-of-the-art literature in the realm of AVs. Particularly, we examined some cyber-attacks and compared their promising mitigation strategies from our perspective. Then, we focused on two security issues involving AVCC: software protection and authentication. For the first problem, our concern is protecting client’s programs executed on remote AVCC resources. Such a usage scenario is susceptible to information leakage and reverse-engineering. Hence, we proposed compiler-based obfuscation techniques. What distinguishes our techniques, is that they are generic and software-based and utilize the intermediate representation, hence, they are platform agnostic, hardware independent and support different high level programming languages. Our results demonstrate that the control-flow of obfuscated code versions are more complicated making it unintelligible for timing side-channels. For the second problem, we focus on protecting AVCC from unauthorized access or intrusions, which may cause misuse or service disruptions. Therefore, we propose a strong privacy-aware authentication technique for users accessing AVCC services or vehicle sharing their resources with the AVCC. Our technique modifies robust function encryption, which protects stakeholder’s confidentiality and withstands linkability and “known-ciphertexts” attacks. Thus, we utilize an authentication server to search and match encrypted data by performing dot product operations. Additionally, we developed another lightweight technique, based on KNN algorithm, to authenticate vehicles at computationally limited charging stations using its owner’s encrypted iris data. Our security and privacy analysis proved that our schemes achieved privacy-preservation goals. Our experimental results showed that our schemes have reasonable computation and communications overheads and efficiently scalable

Overcoming the Intuition Wall: Measurement and Analysis in Computer Architecture

These are exciting times for computer architecture research. Today there is significant demand to improve the performance and energy-efficiency of emerging, transformative applications which are being hammered out by the hundreds for new computing platforms and usage models. This booming growth of applications and the variety of programming languages used to create them is challenging our ability as architects to rapidly and rigorously characterize these applications. Concurrently, hardware has become more complex with the emergence of accelerators, multicore systems, and heterogeneity caused by further divergence between processor market segments. No one architect can now understand all the complexities of many systems and reason about the full impact of changes or new applications. To that end, this dissertation presents four case studies in quantitative methods. Each case study attacks a different application and proposes a new measurement or analytical technique. In each case study we find at least one surprising or unintuitive result which would likely not have been found without the application of our method