Reviewerly/exHarmony huggingface dataset:
MASCARA : Systematically Generating Memorable And Secure Passphrases

Passwords are the most common mechanism for authenticating users online. However, studies have shown that users find it difficult to create and manage secure passwords. To that end, passphrases are often recommended as a usable alternative to passwords, which would potentially be easy to remember and hard to guess. However, as we show, user-chosen passphrases fall short of being secure, while state-of-the-art machine-generated passphrases are difficult to remember.
POSTER: On searching information leakage of Python model execution to detect adversarial examples

The predictive capabilities of machine learning models have improved significantly in recent years, leading to their widespread use in various fields. However, these models remain vulnerable to adversarial attacks, where carefully crafted inputs can mislead predictions and compromise the security of critical systems. Therefore, it is crucial to develop effective methods for detecting and preventing such attacks. Given that many neural network models are implemented using Python, this study addresses the issue of detecting adversarial examples from a new perspective by investigating information leakage in their Python model executions. To realize this objective, we propose a novel Python interpreter that utilizes Python bytecode instrumentation to profile layer-wise instruction-level program executions. We then search for information leakage on both legal and adversarial inputs, identifying their side-channel differences in call executions (i.e., call count, return values, and execution time) and synthesize the detection rule accordingly. Our approach is evaluated against TorchAttacks, AdvDoor, and RNN-Test attacks, targeting various models and applications. Our findings indicate that while there is call-return-value leakage on TorchAttacks images, there is no leakage to detect AdvDoor and RNN-Test attacks based on execution time or return values of string, integer, float, and Boolean type functions.
Securing Container-based Clouds with Syscall-aware Scheduling

Container-based clouds—in which containers are the basic unit of isolation—face security concerns because, unlike Virtual Machines, containers directly interface with the underlying highly privileged kernel through the wide and vulnerable system call interface. Regardless of whether a container itself requires dangerous system calls, a compromised or malicious container sharing the host (a bad neighbor) can compromise the host kernel using a vulnerable syscall, thereby compromising all other containers sharing the host.
#DM-Me: Susceptibility to Direct Messaging-Based Scams

In an emerging scam on social media platforms, cyber-miscreants are luring users into sending them a direct-message (DM) and are subsequently exploiting the messaging channel. We term this attack approach as the DM-Me scam. We report on a survey of 214 MTurk participants, in which we make the first effort to systematically study the susceptibility of users in falling victim to DM-Me scams. We find that most participants chose to send a direct message to at least one scammer, and made such choices more than half the time. This susceptibility can be attributed to the misplaced trust in scammers and the lack of negative consequences foreseen by participants in messaging accounts that they do not fully trust. Interestingly, our results also suggest that women mostly from the 31-40 age-group and who predominantly use Instagram a few times a week are less susceptible than men to financial DM-Me scams as they appear to face more discomfort in initiating a conversation with unfamiliar accounts for such services. We conclude with future research directions in mitigating the risks posed by DM-Me scammers, specifically by developing reliable indicators to aid users in assessing the trustworthiness of an account.
Going Haywire: False Friends in Federated Learning and How to Find Them

Federated Learning (FL) promises to offer a major paradigm shift in the way deep learning models are trained at scale, yet malicious clients can surreptitiously embed backdoors into models via trivial augmentation on their own subset of the data. This is especially true in small- and medium-scale FL systems, which consist of dozens, rather than millions, of clients. In this work, we investigate a novel attack scenario for an FL architecture consisting of multiple non-i.i.d. silos of data in which each distribution has a unique backdoor attacker and where the model convergences of adversaries are not more similar than those of benign clients. We propose a new method, dubbed Haywire, as a security-in-depth approach to respond to this novel attack scenario. Our defense utilizes a combination of kPCA dimensionality reduction of fully-connected layers in the network, KMeans anomaly detection to drop anomalous clients, and server aggregation robust to outliers via the Geometric Median. Our solution prevents the contamination of the global model despite having no access to the backdoor triggers. We evaluate the performance of Haywire from model-accuracy, defense-performance, and attack-success perspectives against multiple baselines. Through an extensive set of experiments, we find that Haywire produces the best performances at preventing backdoor attacks while simultaneously not unfairly penalizing benign clients. We carried out additional in-depth experiments across multiple runs that demonstrate the reliability of Haywire.
Overdrive LowGear 2.0: Reduced-Bandwidth MPC without Sacrifice

Some of the most efficient protocols for Multi-Party Computation (MPC) follow a two-phase approach where correlated randomness, in particular Beaver triples, is generated in the offline phase and then used to speed up the online phase. Recently, more complex correlations have been introduced to optimize certain operations even further, such as matrix triples for matrix multiplications. In this paper, our goal is to improve the efficiency of the triple generation in general and in particular for classical field values as well as matrix operations. To this end, we modify the Overdrive LowGear protocol to remove the costly sacrificing step and therewith reduce the round complexity and the bandwidth. We extend the state-of-the-art MP-SPDZ implementation with our new protocols and show that the new offline phase outperforms state-of-the-art protocols for the generation of Beaver triples and matrix triples. For example, we save in bandwidth compared to Overdrive LowGear.
FlushTime: Towards Mitigating Flush-based Cache Attacks via Collaborating Flush Instructions and Timers on ARMv8-A

ARMv8-A processors generally utilize optimization techniques such as multi-layer cache, out-of-order execution and branch prediction to improve performance. These optimization techniques are inevitably threatened by cache-related attacks including Flush+Reload, Flush+Flush, Meltdown, Spectre, and their variants. These attacks can break the isolation boundaries between different processes or even between user and kernel spaces. Researchers proposed many defense schemes to resist these cache-related attacks. However, they either need to modify the hardware architecture, have incomplete coverage, or introduce significant performance overhead.
ThreadLock: Native Principal Isolation Through Memory Protection Keys

Inter-process isolation has been deployed in operating systems for decades, but secure intra-process isolation remains an active research topic. Achieving secure intra-process isolation within an operating system process is notoriously difficult. However, viable solutions that securely consolidate workloads into the same process have the potential to be extremely valuable.
A New Look at Blockchain Leader Election: Simple, Efficient, Sustainable and Post-Quantum

In this work, we study the blockchain leader election problem. The purpose of such protocols is to elect a leader who decides on the next block to be appended to the blockchain, for each block proposal round. Solutions to this problem are vital for the security of blockchain systems. We introduce an efficient blockchain leader election method with security based solely on standard assumptions for cryptographic hash functions (rather than public-key cryptographic assumptions) and that does not involve a racing condition as in Proof-of-Work based approaches. Thanks to the former feature, our solution provides the highest confidence in security, even in the post-quantum era. A particularly scalable application of our solution is in the Proof-of-Stake setting, and we investigate our solution in the Algorand blockchain system. We believe our leader election approach can be easily adapted to a range of other blockchain settings.
Secure Context Switching of Masked Software Implementations

Cryptographic software running on embedded devices requires protection against physical side-channel attacks such as power analysis. Masking is a widely deployed countermeasure against these attacks and is directly implemented on algorithmic level. Many works study the security of masked cryptographic software on CPUs, pointing out potential problems on algorithmic/microarchitecture-level, as well as corresponding solutions, and even show masked software can be implemented efficiently and with strong (formal) security guarantees. However, these works also make the implicit assumption that software is executed directly on the CPU without any abstraction layers in-between, i.e., they focus exclusively on the bare-metal case. Many practical applications, including IoT and automotive/industrial environments, require multitasking embedded OSs on which masked software runs as one out of many concurrent tasks. For such applications, the potential impact of events like context switches on the secure execution of masked software has not been studied so far at all.
Flag: A Framework for Lightweight Robust Secure Aggregation

In this work, we introduce a lightweight secure aggregation protocol that guarantees liveness (i.e., guaranteed output delivery), robust against faulty inputs and security against malicious clients. First, we improve upon prior works in the “star”-like topology network with a central coordinating (also output) party, Bonawitz et al. (ACM CCS 2017) and Bell et al. (ACM CCS 2020), which are not robust against faulty inputs. Recent works, RoFL (Burkhalter et al.) and (concurrent work) ACORN (Bell et al.) show how to rely on zero-knowledge proofs to address such attacks at expense of significantly high computation costs. We also compare our protocol against the PRIO system by Gibbs and Boneh (USENIX 2017) which achieves the same task in an incomparable security model. We benchmark our protocol with implementation and demonstrate its concrete efficiency. Our solution scales to 1000s of clients, requires only a constant number of rounds, outperforms prior work in computational cost, and has competitive communication cost.
On the Cryptographic Fragility of the Telegram Ecosystem

Telegram is a popular messenger with more than 550 million active users per month and with a large ecosystem of different clients. The wide adoption of Telegram by protestors relying on private and secure messaging provides motivation for developing a profound understanding of its cryptographic design and how this influences its security properties. Telegram has its own bespoke transport layer security protocol, MTProto 2.0. This protocol was recently subjected to a detailed study by Albrecht et al. (IEEE S&P 2022). They gave attacks on the protocol and its implementations, along with a security proof for a modified version of the protocol.
A Trade-off SVP-solving Strategy based on a Sharper pnj-BKZ Simulator

The lattice-based cryptography is one of the most promising candidates in the era of post-quantum cryptography. It is necessary to precisely choose the practical parameters by evaluating the hardness of the underlying hard mathematical problems, such as the shortest vector problem (SVP). Currently, there are two state-of-the-art strategies for solving (approximate) SVP. One is the SVP-solving strategy proposed in G6K[5], which has the least solving time cost but high memory cost requirements; another is to execute progressive BKZ (pBKZ)[8] for pre-processing at first and call the high-dimensional SVP-oracle to find the short vector on the original lattice. Due to the strong pre-processing on the lattice basis, the memory cost of the latter strategy is usually smaller than that of the former strategy, while the time cost of pre-processing is relatively costly. In this paper, we first optimize the pnj-BKZ simulator when the jump value is quite large by giving a refined dimension for free (d4f) estimation. Then, based on our optimized pnj-BKZ simulator, we show a more accurate hardness estimation of LWE by considering technologies such as progressive BKZ pre-processing technology, jump strategy, and d4f technology. Furthermore, based on the sharper pnj-BKZ simulator, we propose an SVP-solving strategy trade-off between G6K and pBKZ, which derives less time cost than pBKZ within less memory compared with G6K. Experimental results show that when solving the TU Darmstadt SVP challenge, our algorithm can save 50%-66% of memory compared with G6K's default SVP-solving strategy. Moreover, our algorithm speeds up the pre-processing stage by 7-30 times, saving the time cost by 4-6 times compared with the pBKZ default SVP-solving strategy. Using our proposed strategy, we solved the 170-dimensional TU Darmstadt SVP challenge and up to the 176-dimensional ideal lattice challenge.
Secure and Efficient Mobile DNN Using Trusted Execution Environments

Many mobile applications have resorted to deep neural networks (DNNs) because of their strong inference capabilities. Since both input data and DNN architectures could be sensitive, there is an increasing demand for secure DNN execution on mobile devices. Towards this end, hardware-based trusted execution environments on mobile devices (mobile TEEs), such as ARM TrustZone, have recently been exploited to execute CNN securely. However, running entire DNNs on mobile TEEs is challenging as TEEs have stringent resource and performance constraints. In this work, we develop a novel mobile TEE-based security framework that can efficiently execute the entire DNN in a resource-constrained mobile TEE with minimal inference time overhead. Specifically, we propose a progressive pruning to gradually identify and remove the redundant neurons from a DNN while maintaining a high inference accuracy. Next, we develop a memory optimization method to deallocate the memory storage of the pruned neurons utilizing the low-level programming technique. Finally, we devise a novel adaptive partitioning method that divides the pruned model into multiple partitions according to the available memory in the mobile TEE and loads the partitions into the mobile TEE separately with a minimal loading time overhead. Our experiments with various DNNs and open-source datasets demonstrate that we can achieve 2-30 times less inference time with comparable accuracy compared to existing approaches securing entire DNNs with mobile TEE.
POSTER: ML-Compass: A Comprehensive Assessment Framework for Machine Learning Models

Machine learning models have made significant breakthroughs across various domains. However, it is crucial to assess these models to obtain a complete understanding of their capabilities and limitations and ensure their effectiveness and reliability in solving real-world problems. In this paper, we present a framework, termed ML-Compass, that covers a broad range of machine learning abilities, including utility evaluation, neuron analysis, robustness evaluation, and interpretability examination. We use this framework to assess seven state-of-the-art classification models on four benchmark image datasets. Our results indicate that different models exhibit significant variation, even when trained on the same dataset. This highlights the importance of using the assessment framework to comprehend their behavior.
A Transformer-based Function Symbol Name Inference Model from an Assembly Language for Binary Reversing

Reverse engineering of a stripped binary has a wide range of applications, yet it is challenging mainly due to the lack of contextually useful information within. Once debugging symbols (e.g., variable names, types, function names) are discarded, recovering such information is not technically viable with traditional approaches like static or dynamic binary analysis. We focus on a function symbol name recovery, which allows a reverse engineer to gain a quick overview of an unseen binary. The key insight is that a well-developed program labels a meaningful function name that describes its underlying semantics well. In this paper, we present AsmDepictor, the Transformer-based framework that generates a function symbol name from a set of assembly codes (i.e., machine instructions), which consists of three major components: binary code refinement, model training, and inference. To this end, we conduct systematic experiments on the effectiveness of code refinement that can enhance an overall performance. We introduce the per-layer positional embedding and Unique-softmax for AsmDepictor so that both can aid to capture a better relationship between tokens. Lastly, we devise a novel evaluation metric tailored for a short description length, the Jaccard* score. Our empirical evaluation shows that the performance of AsmDepictor by far surpasses that of the state-of-the-art models up to around 400%. The best AsmDepictor model achieves an F1 of 71.5 and Jaccard* of 75.4.
BFU: Bayesian Federated Unlearning with Parameter Self-Sharing

As the right to be forgotten has been legislated worldwide, many studies attempt to design machine unlearning mechanisms to enable data erasure from a trained model. Existing machine unlearning studies focus on centralized learning, where the server can access all users' data. However, in a popular scenario, federated learning (FL), the server cannot access users' training data. In this paper, we investigate the problem of machine unlearning in FL. We formalize a federated unlearning problem and propose a bayesian federated unlearning (BFU) approach to implement unlearning for a trained FL model without sharing raw data with the server. Specifically, we first introduce an unlearning rate in BFU to balance the trade-off between forgetting the erased data and remembering the original global model, making it adaptive to different unlearning tasks. Then, to mitigate accuracy degradation caused by unlearning, we propose BFU with parameter self-sharing (BFU-SS). BFU-SS considers data erasure and maintaining learning accuracy as two tasks and optimizes them together during unlearning. Extensive comparisons between our methods and the state-of-art federated unlearning method demonstrate the superiority of our proposed realizations.
LDL: A Defense for Label-Based Membership Inference Attacks

The data used to train deep neural network (DNN) models in applications such as healthcare and finance typically contain sensitive information. A DNN model may suffer from overfitting– it will perform very well on samples seen during training, and poorly on samples not seen during training. Overfitted models have been shown to be susceptible to query-based attacks such as membership inference attacks (MIAs). MIAs aim to determine whether a sample belongs to the dataset used to train a classifier (members) or not (nonmembers). Recently, a new class of label-based MIAs (LAB MIAs) was proposed, where an adversary was only required to have knowledge of predicted labels of samples. LAB MIAs used the insight that member samples were typically located farther away from a classification decision boundary than nonmembers, and were shown to be highly effective across multiple datasets. Developing a defense against an adversary carrying out a LAB MIA on DNN models that cannot be retrained remains an open problem.
Data Privacy Examination against Semi-Supervised Learning

Semi-supervised learning, which learns with only a small amount of labeled data while collecting voluminous unlabeled data to aid its training, has achieved promising performance lately, but it also raises a serious privacy concern: Whether a user’s data has been collected for use without authorization. In this paper, we propose a novel membership inference method against semi-supervised learning, serving to protect user data privacy. Due to involving both the labeled and unlabeled data, the membership patterns of semi-supervised learning’s training data cannot be well captured by the existing membership inference solutions. To this end, we propose two new metrics, i.e., inter-consistency and intra-entropy, tailored specifically to the semi-supervised learning paradigm, able to respectively measure the similarity and calculate the cross-entropy among prediction vectors from the perturbed versions. By exploiting the two metrics for membership inference, our method can dig out membership patterns imprinted on prediction outputs of semi-supervised learning models, thus facilitating effective membership inference. Extensive experiments have been conducted for comparing our method with five rectified baseline inference techniques across four datasets on six semi-supervised learning algorithms. Experimental results exhibit that our inference method achieves over 80% accuracy under each experimental setting, substantially outperforming all baseline techniques.
Payment with Dispute Resolution: A Protocol for Reimbursing Frauds Victims

An “Authorised Push Payment” (APP) fraud refers to a case where fraudsters deceive a victim to make payments to bank accounts controlled by them. The total amount of money stolen via APP frauds is swiftly growing. Although regulators have provided guidelines to improve victims’ protection, the guidelines are vague, the implementation is lacking in transparency, and the victims are not receiving sufficient protection. To facilitate victims’ reimbursement, in this work, we propose a protocol called “Payment with Dispute Resolution” (PwDR) and formally define it. The protocol lets an honest victim prove its innocence to a third-party dispute resolver while preserving the protocol participants’ privacy. It makes black-box use of a standard online banking system. We implement its most computationally-intensive subroutine and analyse its runtime. We also evaluate its asymptotic cost. Our evaluation indicates that the protocol is efficient. It imposes only O(1) overheads to the customer and bank. Moreover, it takes a dispute resolver only 0.09 milliseconds to settle a dispute between the two parties.
Boost Off/On-Manifold Adversarial Robustness for Deep Learning with Latent Representation Mixup

Deep neural networks excel at solving intuitive tasks that are hard to describe formally, such as classification, but are easily deceived by maliciously crafted samples, leading to misclassification. Recently, it has been observed that the attack-specific robustness of models obtained through adversarial training does not generalize well to novel or unseen attacks. While data augmentation through mixup in the input space has been shown to improve the generalization and robustness of models, there has been limited research progress on mixup in the latent space. Furthermore, almost no research on mixup has considered the robustness of models against emerging on-manifold adversarial attacks. In this paper, we first design a latent-space data augmentation strategy called dual-mode manifold interpolation, which allows for interpolating disentangled representations of source samples in two modes: convex mixing and binary mask mixing, to synthesize semantic samples. We then propose a resilient training framework, LatentRepresentationMixup (LarepMixup), that employs mixed examples and softlabel-based cross-entropy loss to refine the boundary. Experimental investigations on diverse datasets (CIFAR-10, SVHN, ImageNet-Mixed10) demonstrate that our approach delivers competitive performance in training models that are robust to off/on-manifold adversarial example attacks compared to leading mixup training techniques.