CaSE: Cache-Assisted Secure Execution on ARM Processors

Recognizing the pressing demands to secure embedded applications, ARM TrustZone has been adopted in both academic research and commercial products to protect sensitive code and data in a privileged, isolated execution environment. However, the design of TrustZone cannot prevent physical memory disclosure attacks such as cold boot attack from gaining unrestricted read access to the sensitive contents in the dynamic random access memory (DRAM). A number of system-on-chip (SoC) bound execution solutions have been proposed to thaw the cold boot attack by storing sensitive data only in CPU registers, CPU cache or internal RAM. However, when the operating system, which is responsible for creating and maintaining the SoC-bound execution environment, is compromised, all the sensitive data is leaked. In this paper, we present the design and development of a cache-assisted secure execution framework, called CaSE, on ARM processors to defend against sophisticated attackers who can launch multi-vector attacks including software attacks and hardware memory disclosure attacks. CaSE utilizes TrustZone and Cache-as-RAM technique to create a cache-based isolated execution environment, which can protect both code and data of security-sensitive applications against the compromised OS and the cold boot attack. To protect the sensitive code and data against cold boot attack, applications are encrypted in memory and decrypted only within the processor for execution. The memory separation and the cache separation provided by TrustZone are used to protect the cached applications against compromised OS. We implement a prototype of CaSE on the i.MX53 running ARM Cortex-A8 processor. The experimental results show that CaSE incurs small impacts on system performance when executing cryptographic algorithms including AES, RSA, and SHA1

Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction

Federated learning is known for its capability to safeguard participants' data privacy. However, recently emerged model inversion attacks (MIAs) have shown that a malicious parameter server can reconstruct individual users' local data samples through model updates. The state-of-the-art attacks either rely on computation-intensive search-based optimization processes to recover each input batch, making scaling difficult, or they involve the malicious parameter server adding extra modules before the global model architecture, rendering the attacks too conspicuous and easily detectable. To overcome these limitations, we propose Scale-MIA, a novel MIA capable of efficiently and accurately recovering training samples of clients from the aggregated updates, even when the system is under the protection of a robust secure aggregation protocol. Unlike existing approaches treating models as black boxes, Scale-MIA recognizes the importance of the intricate architecture and inner workings of machine learning models. It identifies the latent space as the critical layer for breaching privacy and decomposes the complex recovery task into an innovative two-step process to reduce computation complexity. The first step involves reconstructing the latent space representations (LSRs) from the aggregated model updates using a closed-form inversion mechanism, leveraging specially crafted adversarial linear layers. In the second step, the whole input batches are recovered from the LSRs by feeding them into a fine-tuned generative decoder. We implemented Scale-MIA on multiple commonly used machine learning models and conducted comprehensive experiments across various settings. The results demonstrate that Scale-MIA achieves excellent recovery performance on different datasets, exhibiting high reconstruction rates, accuracy, and attack efficiency on a larger scale compared to state-of-the-art MIAs

On throughput efficiency of geographic opportunistic routing in multihop wireless networks

Geographic opportunistic routing (GOR) is a new routing concept in multihop wireless networks. In stead of picking one node to forward a packet to, GOR forwards a packet to a set of candidate nodes and one node is selected dynam-ically as the actual forwarder based on the instantaneous wireless channel condition and node position and availabil-ity at the time of transmission. GOR takes advantages of the spatial diversity and broadcast nature of wireless com-munications and is an efficient mechanism to combat the unreliable links. The existing GOR schemes typically in-volve as many as available next-hop neighbors into the local opportunistic forwarding, and give the nodes closer to the destination higher relay priorities. In this paper, we focus on realizing GOR’s potential in maximizing throughput. We start with an insightful analysis of various factors and their impact on the throughput of GOR, and propose a local met-ric named expected one-hop throughput (EOT) to balance the tradeoff between the benefit (i.e., packet advancement and transmission reliability) and the cost (i.e., medium time delay). We identify an upper bound of EOT and proof its concavity. Based on the EOT, we also propose a local can-didate selection and prioritization algorithm. Simulation re-sults validate our analysis and show that the metric EOT leads to both higher one-hop and path throughput than the corresponding pure GOR and geographic routing

New Algorithms for Secure Outsourcing of Modular Exponentiations

With the rapid development in availability of cloud services, the techniques for securely outsourcing the prohibitively expensive computations to untrusted servers are getting more and more attentions in the scientific community. Exponentiations modulo a large prime have been considered the most expensive operation in discrete-logarithm based cryptographic protocols, and the computationally limited devices such as RFID tags or smartcard may be incapable to accomplish these operations. Therefore, it is meaningful to present an efficient method to securely outsource most of this work-load to (untrusted) cloud servers. In this paper, we propose a new secure outsourcing algorithm for (variable-exponent, variable-base) exponentiation modular a prime in the two untrusted program model. Compared with the state-of-the-art algorithm \cite{HL05}, the proposed algorithm is superior in both efficiency and checkability. We then utilize this algorithm as a subroutine to achieve outsource-secure Cramer-Shoup encryptions and Schnorr signatures. Besides, we propose the first outsource-secure and efficient algorithm for simultaneous modular exponentiations. Moreover, we formally prove that both the algorithms can achieve the desired security notions. We also provide the experimental evaluation that demonstrates the efficiency and effectiveness of the proposed outsourcing algorithms and schemes

Enabling Public Verifiability and Data Dynamics for Storage Security

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public verifiability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the Proof of Retrievability model \cite{Shacham:ASIACRYPT:2008} by manipulating the classic Merkle Hash Tree (MHT) construction for block tag authentication. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure

TruSpy: Cache Side-Channel Information Leakage from the Secure World on ARM Devices

As smart, embedded devices are increasingly integrated into our daily life, the security of these devices has become a major concern. The ARM processor family, which powers more than 60% of embedded devices, introduced TrustZone technology to offer security protection via an isolated execution environment called secure world. Caches in TrustZone-enabled processors are extended with a non-secure (NS) bit to indicate whether a cache line is used by the secure world or the normal world. This cache design improves system performance by eliminating the need to perform cache flush during world switches; however, it also enables cache contention between the two worlds. In this work, we present TruSpy, the first study of timingbased cache side-channel information leakage of TrustZone. Our proposed attack exploits the cache contention between normal world and secure world to recover secret information from secure world. Two attacks are proposed in TruSpy, namely, the normal world OS attack and the normal world Android app attack. In the OS-based attack, the attacker is able to access virtual-to-physical address translation and high precision timers. In the Android app-based attack, these tools are unavailable to the attacker, so we devise a novel method that uses the expected channel statistics to allocate memory for cache probing. We also show how an attacker might use the less accurate performance event interface as a timer. Using the T-table based AES implementation in OpenSSL 1.0.1f as an example, we demonstrate that it is possible for a normal world attacker to steal a fine-grained secret from the secure world using a timing-based cache side-channel. We can recover the full AES encryption key via either the OSbased attack or the Android app-based attack. Since our zero permission TruSpy attack is based on the cache design in TrustZone enabled ARM processors, it poses a significant threat to a wide array of devices. To mitigate the newly discovered threat, we also propose both application-based and system-oriented countermeasures

The relationship between fundamental motor skills and physical fitness in preschoolers: a short-term longitudinal study

PurposePhysical fitness and fundamental motor skills are two important aspects for the healthy development of preschoolers. Despite the growing interest in clarifying their relationship, the scarcity of longitudinal studies prevents us from understanding causality.MethodThis study employed a cross-lagged model with two time points to investigate the bidirectional relationship between these two aspects. A total of 174 preschoolers (54.0% girls) from 3 to 6 years old (M = 3.96 ± 0.47) were surveyed, they were recruited by convenience from two kindergartens in Beijing, China, and their physical fitness (via CNPFDSM-EC) and fundamental motor skills (via TGMD-3) were tracked over a period of 6 months.ResultsThe findings revealed a bidirectional predictive effect. The predictive strength of flexibility was found to be lower than other physical fitness aspects, while locomotor skills demonstrated a higher predictive strength than object control skills.ConclusionThis study indicates that physical fitness and fundamental motor skills mutually enhance each other in young children, and both should be emphasized in preschool sports education