Visualizing size-security tradeoffs for lattice-based encryption

There are many proposed lattice-based encryption systems. How do these systems compare in the security that they provide against known attacks, under various limits on communication volume? There are several reasons to be skeptical of graphs that claim to answer this question. Part of the problem is with the underlying data points, and part of the problem is with how the data points are converted into graphs

Decryption failure is more likely after success

The user of an imperfectly correct lattice-based public-key encryption scheme leaks information about their secret key with each decryption query that they answer---even if they answer all queries successfully. Through a refinement of the D\u27Anvers--Guo--Johansson--Nilsson--Vercauteren--Verbauwhede failure boosting attack, we show that an adversary can use this information to improve his odds of finding a decryption failure. We also propose a new definition of $\delta$-correctness, and we re-assess the correctness of several submissions to NIST\u27s post-quantum standardization effort

Effectiveness of Virtualization in the Process of Running a Virtual Instance of a Computer System in a Layer Abstracted from the Actual Hardware

oai:ojs2.ojs.technoaretepublication.org:article/9Abstract - Under this research paper many different kinds of viewpoints concept of Virtualizations, the use ofvisualization for security reasons, and the Importance of IoT in the virtualization process. Moreover, this paper willsignify that Different virtual environments are offered through a virtualized IT firm, which has to be accepted andoperated if technology evolves quickly. This research paper will highlight many different types of strong andsustainable viewpoints about the visualization process and its impacts. It can be a supportive way for the nextresearcher to understand the scope of this research paper in a proper manner. In order to conduct a properunderstanding of the effect of virtualization in running a virtual instance of a computer system from actualhardware will be the main aim of this paper. On the other hand, virtualization may also be described as a techniquethat can theoretically divide a server's physical resources and use it as several, separated computers known asvirtual machines

A discretization attack

This paper presents an attack against common procedures for comparing the size-security tradeoffs of proposed cryptosystems. The attack begins with size-security tradeoff data, and then manipulates the presentation of the data in a way that favors a proposal selected by the attacker, while maintaining plausible deniability for the attacker. As concrete examples, this paper shows two manipulated comparisons of size-security tradeoffs of lattice-based encryption proposals submitted to the NIST Post-Quantum Cryptography Standardization Project. One of these manipulated comparisons appears to match public claims made by NIST, while the other does not, and the underlying facts do not. This raises the question of whether NIST has been subjected to this attack. This paper also considers a weak defense and a strong defense that can be applied by standards-development organizations and by other people comparing cryptographic algorithms. The weak defense does not protect the integrity of comparisons, although it does force this type of attack to begin early. The strong defense stops this attack

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. While prior efforts recommend moving to custom accelerators to accelerate FHE computing, these solutions lack cost-effectiveness and scalability. In this work, we leverage GPUs to accelerate FHE, capitalizing on a well-established GPU ecosystem that is 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 and improving performance. 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 improves FHE workload performance, exploiting 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×, 14.2×, and 2.3× over Intel Xeon CPU, NVIDIA V100 GPU, and Xilinx FPGA implementations, respectively

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

Post-Quantum Authentication in TLS 1.3: A Performance Study

The potential development of large-scale quantum computers is raising concerns among IT and security research professionals due to their ability to solve (elliptic curve) discrete logarithm and integer factorization problems in polynomial time. All currently used public key algorithms would be deemed insecure in a post-quantum (PQ) setting. In response, the National Institute of Standards and Technology (NIST) has initiated a process to standardize quantum-resistant crypto algorithms, focusing primarily on their security guarantees. Since PQ algorithms present significant differences over classical ones, their overall evaluation should not be performed out-of-context. This work presents a detailed performance evaluation of the NIST signature algorithm candidates and investigates the imposed latency on TLS 1.3 connection establishment under realistic network conditions. In addition, we investigate their impact on TLS session throughput and analyze the trade-off between lengthy PQ signatures and computationally heavy PQ cryptographic operations. Our results demonstrate that the adoption of at least two PQ signature algorithms would be viable with little additional overhead over current signature algorithms. Also, we argue that many NIST PQ candidates can effectively be used for less time-sensitive applications, and provide an in-depth discussion on the integration of PQ authentication in encrypted tunneling protocols, along with the related challenges, improvements, and alternatives. Finally, we propose and evaluate the combination of different PQ signature algorithms across the same certificate chain in TLS. Results show a reduction of the TLS handshake time and a significant increase of a server\u27s TLS tunnel connection rate over using a single PQ signature scheme

Security Infrastructure Technology for Integrated Utilization of Big Data

This open access book describes the technologies needed to construct a secure big data infrastructure that connects data owners, analytical institutions, and user institutions in a circle of trust. It begins by discussing the most relevant technical issues involved in creating safe and privacy-preserving big data distribution platforms, and especially focuses on cryptographic primitives and privacy-preserving techniques, which are essential prerequisites. The book also covers elliptic curve cryptosystems, which offer compact public key cryptosystems; and LWE-based cryptosystems, which are a type of post-quantum cryptosystem. Since big data distribution platforms require appropriate data handling, the book also describes a privacy-preserving data integration protocol and privacy-preserving classification protocol for secure computation. Furthermore, it introduces an anonymization technique and privacy risk evaluation technique. This book also describes the latest related findings in both the living safety and medical fields. In the living safety field, to prevent injuries occurring in everyday life, it is necessary to analyze injury data, find problems, and implement suitable measures. But most cases don’t include enough information for injury prevention because the necessary data is spread across multiple organizations, and data integration is difficult from a security standpoint. This book introduces a system for solving this problem by applying a method for integrating distributed data securely and introduces applications concerning childhood injury at home and school injury. In the medical field, privacy protection and patient consent management are crucial for all research. The book describes a medical test bed for the secure collection and analysis of electronic medical records distributed among various medical institutions. The system promotes big-data analysis of medical data with a cloud infrastructure and includes various security measures developed in our project to avoid privacy violations

CloudFence: Enabling Users to Audit the Use of their Cloud-Resident Data

One of the primary concerns of users of cloud-based services and applications is the risk of unauthorized access to their private information. For the common setting in which the infrastructure provider and the online service provider are different, end users have to trust their data to both parties, although they interact solely with the service provider. This paper presents CloudFence, a framework that allows users to independently audit the treatment of their private data by third-party online services, through the intervention of the cloud provider that hosts these services. CloudFence is based on a fine-grained data flow tracking platform exposed by the cloud provider to both developers of cloud-based applications, as well as their users. Besides data auditing for end users, CloudFence allows service providers to confine the use of sensitive data in well-defined domains using data tracking at arbitrary granularity, offering additional protection against inadvertent leaks and unauthorized access. The results of our experimental evaluation with real-world applications, including an e-store platform and a cloud-based backup service, demonstrate that CloudFence requires just a few changes to existing application code, while it can detect and prevent a wide range of security breaches, ranging from data leakage attacks using SQL injection, to personal data disclosure due to missing or erroneously implemented access control checks

Nation-State Attackers and their Effects on Computer Security

Nation-state intelligence agencies have long attempted to operate in secret, but recent revelations have drawn the attention of security researchers as well as the general public to their operations. The scale, aggressiveness, and untargeted nature of many of these now public operations were not only alarming, but also baffling as many were thought impossible or at best infeasible at scale. The security community has since made many efforts to protect end-users by identifying, analyzing, and mitigating these now known operations. While much-needed, the security community's response has largely been reactionary to the oracled existence of vulnerabilities and the disclosure of specific operations. Nation-State Attackers, however, are dynamic, forward-thinking, and surprisingly agile adversaries who do not rest on their laurels and are continually advancing their efforts to obtain information. Without the ability to conceptualize their actions, understand their perspective, or account for their presence, the security community's advances will become antiquated and unable to defend against the progress of Nation-State Attackers. In this work, we present and discuss a model of Nation-State Attackers that can be used to represent their attributes, behavior patterns, and world view. We use this representation of Nation-State Attackers to show that real-world threat models do not account for such highly privileged attackers, to identify and support technical explanations of known but ambiguous operations, and to identify and analyze vulnerabilities in current systems that are favorable to Nation-State Attackers.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143907/1/aaspring_1.pd