CCSW '22: The 2022 cloud computing security workshop

Clouds and massive-scale computing infrastructures are starting to dominate computing and will likely continue to do so for the foreseeable future. Major cloud operators are now comprising millions of cores hosting substantial fractions of corporate and government IT infrastructure. CCSW is the world's premier forum bringing together researchers and practitioners in all security aspects of cloud-centric and outsourced computing, including: ·Side channel attacks ·Cryptographic protocols for cloud security ·Secure cloud resource virtualization mechanisms ·Secure data management outsourcing (e.g., database as a service) ·Privacy and integrity mechanisms for outsourcing ·Foundations of cloud-centric threat models ·Secure computation outsourcing ·Remote attestation mechanisms in clouds ·Sandboxing and VM-based enforcements ·Trust and policy management in clouds ·Secure identity management mechanisms ·Cloud-aware web service security paradigms and mechanisms ·Cloud-centric regulatory compliance issues and mechanisms ·Business and security risk models and clouds ·Cost and usability models and their interaction with security in clouds ·Scalability of security in global-size clouds ·Binary analysis of software for remote attestation and cloud protection ·Network security (DOS, IDS etc.) mechanisms for cloud contexts ·Security for emerging cloud programming models ·Energy/cost/efficiency of security in clouds ·mOpen hardware for cloud ·Machine learning for cloud protection CCSW especially encourages novel paradigms and controversial ideas that are not on the above list. The workshop has historically acted as a fertile ground for creative debate and interaction in security-sensitive areas of computing impacted by clouds. This year marked the 13th anniversary of CCSW. In the past decade, CCSW has had a significant impact in our research community

A Theoretical Framework for the Analysis of Physical Unclonable Function Interfaces and its Relation to the Random Oracle Model

Analysis of advanced Physical Unclonable Function (PUF) applications and protocols rely on assuming that a PUF behaves like a random oracle, that is, upon receiving a challenge, a uniform random response with replacement is selected, measurement noise is added, and the resulting response is returned. In order to justify such an assumption, we need to rely on digital interface computation that to some extent remains confidential -- otherwise, information about PUF challenge response pairs leak with which the adversary can train a prediction model for the PUF. We introduce a theoretical framework that allows the adversary to have a prediction model (with a typical accuracy of 75% for predicting response bits for state-of-the-art silicon PUF designs). We do not require any confidential digital computing or digital secrets while we can still prove rigorous statements about the bit security of a system that interfaces with the PUF. In particular, we prove the bit security of a PUF-based random oracle construction; this merges the PUF framework with fuzzy extractors.</p

Gradient descent-type methods: Background and simple unified convergence analysis

In this book chapter, we briefly describe the main components that constitute the gradient descent method and its accelerated and stochastic variants. We aim at explaining these components from a mathematical point of view, including theoretical and practical aspects, but at an elementary level. We will focus on basic variants of the gradient descent method and then extend our view to recent variants, especially variance-reduced stochastic gradient schemes (SGD). Our approach relies on revealing the structures presented inside the problem and the assumptions imposed on the objective function. Our convergence analysis unifies several known results and relies on a general, but elementary recursive expression. We have illustrated this analysis on several common schemes

Programmable access-controlled and generic erasable PUF design and its applications

Physical unclonable functions (PUFs) have not only been suggested as a new key storage mechanism, but—in the form of so-called strong PUFs—also as cryptographic primitives in advanced schemes, including key exchange, oblivious transfer, or secure multi-party computation. This notably extends their application spectrum, and has led to a sequence of publications at leading venues such as IEEE S&P, CRYPTO, and EUROCRYPT in the past. However, one important unresolved problem is that adversaries can break the security of all these advanced protocols if they gain physical access to the employed strong PUFs after protocol completion. It has been formally proven that this issue cannot be overcome by techniques on the protocol side alone, but requires resolution on the hardware level—the only fully effective known countermeasure being so-called erasable PUFs. Building on this work, this paper is the first to describe a generic method of how any given silicon strong PUF with digital CRP-interface can be turned into an erasable PUF. We describe how the strong PUF can be surrounded with a trusted control logic that allows the blocking (or “erasure”) of single CRP. We implement our approach, which we call “GeniePUF,” on FPGA, reporting detailed performance data and practicality figures. Furthermore, we develop the first comprehensive definitional framework for erasable PUFs. Our work so re-establishes the effective usability of strong PUFs in advanced cryptographic applications, and in the realistic case, adversaries get access to the strong PUF after protocol completion. As an extension to earlier versions of this work, we also introduce a generalization of erasable PUFs in this paper, which we call programmable access-controlled PUFs (PAC PUFs). We detail their definition, and discuss various exemplary applications of theirs

Game theoretic mixed experts for combinational adversarial machine learning

Recent advances in adversarial machine learning have shown that defenses considered to be robust are actually susceptible to adversarial attacks which are specifically tailored to target their weaknesses. These defenses include Barrage of Random Transforms (BaRT), Friendly Adversarial Training (FAT), Trash is Treasure (TiT) and ensemble models made up of Vision Transformers (ViTs), Big Transfer models and Spiking Neural Networks (SNNs). A natural question arises: how can one best leverage a combination of adversarial defenses to thwart such attacks? In this paper, we provide a game-theoretic framework for ensemble adversarial attacks and defenses which answers this question. In addition to our framework we produce the first adversarial defense transferability study to further motivate a need for combinational defenses utilizing a diverse set of defense architectures. Our framework is called Game theoretic Mixed Experts (GaME) and is designed to find the Mixed-Nash strategy for a defender when facing an attacker employing compositional adversarial attacks. We show that this framework creates an ensemble of defenses with greater robustness than multiple state-of-the-art, single-model defenses in addition to combinational defenses with uniform probability distributions. Overall, our framework and analyses advance the field of adversarial machine learning by yielding new insights into compositional attack and defense formulations

Generalizing DP-SGD with shuffling and batch clipping

Classical differential private DP-SGD implements individual clipping with random subsampling, which forces a mini-batch SGD approach. We provide a general differential private algorithmic framework that goes beyond DP-SGD and allows any possible first order optimizers (e.g., classical SGD and momentum based SGD approaches) in combination with batch clipping, which clips an aggregate of computed gradients rather than summing clipped gradients (as is done in individual clipping). The framework also admits sampling techniques beyond random subsampling such as shuffling. Our DP analysis follows the f -DP approach and introduces a new proof technique based on a slightly stronger adversarial model which allows us to derive simple closed form expressions and to also analyse group privacy. In particular, for E epochs work and groups of size g, we show a√gE DP dependency for batch clipping with shuffling

Secure Remote Attestation with Strong Key Insulation Guarantees

Recent years have witnessed a trend of secure processor design in both academia and industry. Secure processors with hardware-enforced isolation can be a solid foundation of cloud computation in the future. However, due to recent side-channel attacks, the commercial secure processors failed to deliver the promises of a secure isolated execution environment. Sensitive information inside the secure execution environment always gets leaked via side channels. This work considers the most powerful software-based side-channel attackers, i.e., an All Digital State Observing (ADSO) adversary who can observe all digital states, including all digital states in secure enclaves. Traditional signature schemes are not secure in ADSO adversarial model. We introduce a new cryptographic primitive called One-Time Signature with Secret Key Exposure (OTS-SKE), which ensures no one can forge a valid signature of a new message or nonce even if all secret session keys are leaked. OTS-SKE enables us to sign attestation reports securely under the ADSO adversary. We also minimize the trusted computing base by introducing a secure co-processor into the system, and the interaction between the secure co-processor and the attestation processor is unidirectional. That is, the co-processor takes no inputs from the processor and only generates secret keys for the processor to fetch. Our experimental results show that the signing of OTS-SKE is faster than that of Elliptic Curve Digital Signature Algorithm (ECDSA) used in Intel SGX

Bilinear map based one-time signature scheme with secret key exposure

Dijk et al. [6] presents Remote Attestation (RA) for secure processor technology which is secure in the presence of an All Digital State Observing (ADSO) adversary. The scheme uses a combination of hardware security primitives and design principles together with a new cryptographic primitive called a Public Key Session based One-Time Signature Scheme with Secret Key Exposure (OTS-SKE). [6] shows a hash based realization of OTS-SKE which is post quantum secure but suffers long 8.704 KB signatures for 128-bit quantum security or 256-bit classical security. From a classical cryptographic perspective we complete the picture by introducing a bilinear map based OTS-SKE with short 0.125 KB signatures, 65 times shorter, and for which the security reduces to the Computational Diffie-Hellman Problem (CDHP) – at the cost of a 9× longer initialization phase in the RA scheme if implemented in software (this can be improved with appropriate elliptic curve hardware acceleration). Signing takes 560 ms at most 60% of the > 936 ms needed for the hash based scheme

Autonomous secure remote attestation even when all used and to be used digital keys leak

We provide a new remote attestation scheme for secure processor technology, which is secure in the presence of an All Digital State Observing (ADSO) adversary. To accomplish this, we obfuscate session signing keys using a silicon Physical Unclonable Function (PUF) with an extended interface that combines the LPN-PUF concept with a repetition code for small failure probabilities, and we introduce a new signature scheme that only needs a message dependent subset of a session signing key for computing a signature and whose signatures cannot be successfully forged even if one subset per session signing key leaks. Our solution for remote attestation shows that results computed by enclaves can be properly verified even when an ADSO-adversary is present. For $N=2^l$ sessions, implementation results show that signing takes $934.9+0.6\cdot l$ ms and produces a signature of $8.2+0.03\cdot l$ KB, and verification by a remote user takes $118.2+0.4\cdot l$ ms. During initialization, generation of all session keys takes $819.3 \cdot N$ ms and corresponding storage is $3 \cdot 10^{-5} + 0.12 \cdot N$ MB