Reviewerly/exHarmony huggingface dataset:
Deceiving Learning-based Sketches to Cause Inaccurate Frequency Estimation

Learning-based sketches have been widely studied as an improvement of traditional sketches that achieves high efficiency in terms of both time and space. It uses a learning model to reveal and exploit underlying patterns of input data for helping traditional sketches obtain accurate frequency estimation with memory efficient. However, recent studies only focus on the performance improvement of learning-based sketches and pay little attention to security. The potential security problems can be easily exploited by an adversary to make learning-based sketches inaccurate. In this paper, we firstly explore the security issues of learning-based sketches with regard to estimation accuracy and memory overhead. Some adversarial scenarios of learning model and backup sketch are modeled according to the knowledge and capabilities of an adversary. Then, we propose four attacks to deceive learning-based sketch, namely counterfeit attack, targeted point attack, memory occupation attack, and blind increment attack. We conduct a series of experiments based on real-world datasets and verify that the proposed attacks highly degrade the performance of learning-based sketch even when the adversary knows nothing about it.
An Efficient Certificateless Authentication Scheme for Satellite Internet

Satellite Internet has broad emerging applications in aviation, marine, forest, disaster emergency and other fields. Since the openness of satellite Internet, it is extremely vulnerable to eavesdropping, replay, impersonation and other attacks, which may cause significant privacy and security concerns. To prevent malicious nodes from accessing and attacking satellite Internet, many authentication schemes have been proposed, but few of them consider the authentication and security of satellite nodes. In addition, most schemes assume that key management in network control center (NCC) is absolutely secure. But if it is maliciously attacked and the public-private key pairs are leaked, the entire system will crash immediately. In this paper, we propose CLASSI, a novel, secure and efficient certificateless authentication scheme for satellite Internet. CLASSI no longer assigns the complete public and private keys when users, satellites and gateway stations register, but uses their identity information to generate partial private keys. We make full use of the computing and storage capabilities of satellites to give satellites the ability to authenticate. We theoretically prove the security of our scheme through formal analysis and prove that our scheme can resist various of attacks. We also conducted extensive experiments and compared CLASSI with the existing schemes. The experimental results demonstrate that CLASSI is more suitable for user access in satellite Internet when meeting more security attributes.
EnShare: Sharing Files Securely and Efficiently in the Cloud using Enclave

As the cloud-based file sharing becomes increasingly popular, it is crucial to protect the outsourced data against unauthorized access. In this paper, we propose EnShare, a secure and practical file sharing system that leverages cooperation of server-side and client-side enclaves to enforce access control, with the former responsible for registration, authentication and access control enforcement and the latter performing file decryption. Such design significantly reduces the computation workload of server-side enclaves, thus capable of handling concurrent requests. Meanwhile, it also supports immediate permission revocation, since the file decryption keys inside the client-side enclaves are destroyed immediately after use. We implement a prototype of EnShare and the evaluation demonstrates it enforces access control securely with high throughput and low latency.
RetCom: Information Retrieval-Enhanced Automatic Source-Code Summarization

With the purpose of saving the developing time of software engineers and promoting the work efficiency of programs, the research on automated source-code summarization (SCS) has become necessary in recent years, i.e. generating language descriptions for source code. To date, there exist two categories of SCS methods: information retrieval (IR)-based SCS and neural-based SCS. The latter is the mainstream method at present, however, this line of work suffers from the drawback of incapability to generate low-frequency words, which potentially degrades the performance. To tackle this predicament, we in this paper propose an IR-enhanced neural SCS method RetCom to improve the prediction of low-frequency words through leveraging both structural-level and semantic-level code retrievals. Furthermore, we figure out a token-level context-dependent mixture network to fuse different information sources, i.e. original code, structurally most similar code, and semantically most similar code. Finally, extensive experiments are performed to validate our proposed RetCom using two real-world datasets. Compared to several baseline methods, the experimental results show that our method does validly capture more low-frequency words to conduct a superior performance.
Diabetes Mellitus Type 2 Data Sharing System Based on Blockchain and Attribute-Encryption

Medical data sharing can improve the quality of medical care and promote progress in the field of public health. However, medical data has high confidentiality and complexity. Taking the clinical diagnosis and treatment data of Diabetes Mellitus Type 2 (T2DM) in medical treatment as an example, T2DM data often has the phenomenon of "multi-disease coexistence" and is insecure and inaccurate in the process of sharing. Based on Blockchain and Attribute- Encryption (ABE), we propose a T2DM data sharing system to solve the above problems. An Attribute-Encryption algorithm T2DM-CP-ABE is proposed to solve the problem of excessive sharing granularity. T2DM-CP-ABE, combined with the medical data contribution module, takes the contribution factor as an important consideration, to stimulate the power of T2DM data sharing and promote the development of national diabetes prevention. Based on the Blockchain, a medical data attribute authorization model is proposed, which provides a credible solution for multiple hospitals to realize medical data attribute authorization, and realizes the clinical diagnosis and treatment data sharing of T2DM among doctors. Based on Blockchain and ABE technology, a data sharing system for T2DM has been implemented on the Fabric platform. The result shows that the average time for the system to complete a single sharing is less than 1 second.
Create Persona of Elderly Users by Clustering Analysis of Needs

Through the cluster analysis of the needs of 174 elderly people aged 60-90, combined with the investigation of the basic information, physical condition, and purchase motivation of users, it is used to create the role of elderly users to establish the goal of product design. Four types of models are obtained by cluster analysis: group 1 (n=33) is the population with medium need, group 2 (n=80) is the population with high need, group 3 (n=50) is the population with priority for personal physiological support, and group 4 (n=11) is the population with low need. According to the need analysis, the role analysis is carried out, and the results are compared. The different characteristics of the four groups of needs can have an impact on the product design decisions of the elderly group.
API Misuse Detection Method Based on Transformer

Software developers need to take advantage of a variety of APIs (application programming interface) in their programs to implement specific functions. The problem of API misuses often arises when developers have incorrect understandings about the new APIs without carefully reading API documents. In order to avoid software defects caused by API misuse, researchers have explored multiple methods, including using AI(artificial intelligence) technology.As a kind of neural network in AI, Transformer has a good sequence processing ability, and the self attention mechanism used by Transformer can better catch the relation in a sequence or between different sequences. Besides it has a good model interpretability. From the perspective of combining API misuse detection with AI, this paper implements a standard Transformer model and a target-combination Transformer model to the learning of API usage information in a named API call sequence extracted from API usage program code. Then we present in the paper the way that our models use API usage information to detect if an API is misused in code. We use F1, precision and recall to evaluate the detection ability and show the advantages of our models in these three indexes. Besides, our models based on Transformer both have a better convergence. Finally, this paper explains why the models based on Transformer has a better performance by showing attention weight among different elements in code.
Fine-Tuning Pre-Trained Model to Extract Undesired Behaviors from App Reviews

Mobile application markets usually enact policies to describe in detail the minimum requirements that an application should comply with. User comments on mobile applications contain a large amount of information that can be used to find out APP's violations of market policies in a cost-effective way. Existing state-of-the-art methods match user comments with the violations of market policies based on well-designed syntax rules, which however cannot well capture the semantics of user comments and cannot be generalized to the scenarios not covered by the rules. To address this issue, we propose an innovative method, UBC-BERT, to detect undesired behavior from user comments based on their semantics. By incorporating sentence embeddings with attention, we train a classification model for 21 groups of undesirable behaviors based on the fine-tuning of a pre-trained model BERT-BASE. The experimental results show that our solution outperforms the baseline solutions in terms of a higher precision(up to 60.5% more).
FLightNER: A Federated Learning Approach to Lightweight Named-Entity Recognition

We introduce FLightNER, a Federated Learning (FL) model that extends an existing state-of-the-art Named-Entity Recognition (NER) model using prompt-tuning known as LightNER. FLightNER allows the aggregation of only the trainable parameters of LightNER without model accuracy degradation saving 10 GB per client enabling more clients to join a federation without extending the central server’s memory. We evaluate our approach against two baselines using three diverse datasets with different distributions across up to seven clients in a federation. We empirically show that compared to the centrally-trained LightNER model, FLightNER outperforms it by 19% when performed on a medical dataset with label imbalance across clients and matches it when performed on two balanced datasets: CoNLL and I2B2. Furthermore, we use and evaluate two well-established memory-saving techniques: AdaFactor optimizer and Automatic Mixed Precision on our FL approach. Our findings enable owners of sensitive data such as healthcare practitioners to efficiently train an NER model collaboratively, with low memory requirements, while keeping their data on-premise.
MLIA: modulated LED illumination-based adversarial attack on traffic sign recognition system for autonomous vehicle

Traffic sign recognition (TSR) system is essential for autonomous vehicle and is vulnerable to security threats from adversarial attacks. The existing adversarial attacks for TSR are invasive and suffer from poor concealment and high computational complexity, and thus have low feasibility in real-world scenarios. This paper proposes a non-invasive modulated LED illumination-based adversarial attack scheme. By generating luminance flashes imperceptible to human eyes through fast intensity modulation of lighting such as LED streetlights and exploiting the rolling shutter mechanism of CMOS sensors of in-vehicle imaging system, the proposed attack scheme can successfully perform adversarial attacks on TSR system by implanting luminance information perturbations into the images acquired by autonomous vehicle and thus poisoning the image data fed into TSR system. Depending on the modulation frequency and pattern of LED illumination, the proposed attack scheme enables denial of service (DoS) attack that leads to traffic sign detection failure and escape attack that leads to traffic sign misclassification, with the advantages of superior concealment, low computational complexity and high practical feasibility. Experiments are conducted with two benchmark datasets (GTSDB and GTSRB) and two state-of-the-art models of TSR detection and TSR classification, YOLOv5m and Sill-Net respectively, in both the digital and physical world. Experimental results show that the proposed DoS attack on the TSR detection model (YOLOv5m) can reach the success rate of 90.00% and the proposed escape attack on the TSR classification model (Sill-Net) can achieve the success rate of 35.00%.
WBA: A Warping-based Approach to Generating Imperceptible Adversarial Examples

The human can easily recognize the incongruous parts of an image, for example, perturbations unrelated to the image itself, but are poor at spotting the small geometric transformations. However, in terms of the robustness of deep neural networks (DNNs), the ability to properly recognize objects with small geometric transformations is still a challenge. In this work, we investigate the problem from the perspective of adversarial attacks: does the performance of DNNs degrade even when small geometric transformations are applied to images? To this end, we propose a novel adversarial attack method, called WBA, a Warping-Based Adversarial attack method, which does not introduce information independent of the original images but manipulates the existing pixels of the images by elastic warping transformations to generate adversarial examples that are imperceptible to the human eye. At the same time, existing adversarial attacks typically generate adversarial examples by modifying pixels in the spatial domain of the image, the addition of such perturbations introduces extra information unrelated to the image itself and is easily detected by the naked eyes. We demonstrate the effectiveness of WBA by extensive experiments on commonly used datasets, including MNIST, CIFAR10, and ImageNet. The results show that WBA can quickly generate adversarial examples with the highest adversarial strength, consumes less time, and can be comparable to optimization-based adversarial attack methods in image perception evaluation metrics such as LPIPS, SSIM, and far more than gradient direction-based iterative methods.
A General Backdoor Attack to Graph Neural Networks Based on Explanation Method

Graph neural networks (GNNs) have achieved significant performance in many applications (e.g., social spammer detection and Facebook page classification). Recently, backdoor attacks pose a new security threat to the training process of GNNs. Attackers intend to inject backdoors into GNNs, such that the attacked model performs well on benign samples, whereas its prediction will be changed to target label when the trigger is present. However, existing works on backdoor attacks could not attack arbitrary nodes effectively and achieve high performance on both nodes with discrete features and that with continuous features. In this paper, we propose a general backdoor attack to GNNs which could effectively attack arbitrary nodes while keeping other nodes unaffected as much as possible. We first ensure some important edges for a target node by utilizing edge explanation method and remove these edges if the edge exists before (vice versa). Then we ensure important features by using feature explanation method and use a neural network to optimize these features to obtain feature triggers (i.e., perturbation). Finally, we inject edge and feature triggers into target nodes to implement attack. We evaluate our attack from two aspects including generality and effectiveness. For the first part, our attack is effective on both datasets with discrete features and continuous features. For the second part, we randomly select different node subset with different size (e.g., 20, 50, 100 nodes) as target nodes and our attack achieves higher performance against other two state-of-the-art attacks.
Secure and efficient publicly verifiable ridge regression outsourcing scheme

Ridge regression is an important statistical method that is widely used in real life, such as health prediction. As clients with limited computing resources may not be able to handle large training datasets, it is necessary to outsource this operation to a powerful cloud server. Data privacy is raised due to outsourcing. Some previous works on secure outsourcing ridge regression were provided for privacy preserving. However, there are some issues such as having heavy workloads and inefficient. In this paper, we propose an efficient privacy-preserving protocol for outsourcing ridge regression. In our design, we utilize blinding technology to protect the privacy of clients by transforming the original problem into an encryption problem with orthogonal and diagonal matrices as the secret keys. The following advantages can be seen from the experimental result and theoretical analysis: (1) Our protocol is more efficient than related works. When the calculation dimension is greater than 6000×1500, clients computations greatly reduce to 60 percent of other schemes and the total computing is close to the original calculation; (2) Edge server provides the public verifiability, which allows any verifier to verify whether the result returned by the cloud server is correct; (3) We also give a formal proof of security based on the standard simulation model.
Dynamically Selected Mixup Machine Unlearning

Machine unlearning is a significant part of machine learning security because machine learning models are not immune to attacks such as poisoning attacks. In addition, various studies have proved that it is feasible to obtain training set information from the model, which can lead to leakage of user data privacy. Therefore, there is an urgent need for a method to remove specific data from the model training set. Naive retraining will bring huge time and resource costs, while SISA can achieve a balance between unlearning cost and model performance to a certain extent by dividing the dataset into multiple independent shards. As forgotten data grows, the performance of SISA is still unable to meet the actual needs. In this paper, we propose a method, which dynamically selects the shards that need to be retrained. First, we divide the dataset into multiple independent shards, when the forgotten data makes multiple shards need to be retrained at the same time, our approach is to mix these affected shards together, and then train the mixed shards individually to achieve global forgetting. The simulation results show that compared with SISA, we can get more than 3× speedup on different datasets, and the accuracy of the aggregation model is also improved by 0.5% and 2.1% on MNIST and CIFAR10, respectively.
A New Code Review Method based on Human Errors

Modern code reviews tend to take a lightweight process, in which the accuracy and efficiency of identifying defects rely heavily on code reviewers’ experience. The human errors of developers, as a significant cause of software defects, is a key to identifying defects. However, there is a lack of understanding of the human error mechanisms underlying defects in code. This paper proposes an innovative code review method for identifying defects by pinpointing the scenarios that developers tend to commit errors. The method was validated by a comprehensive experimental study that involved 49 code reviewers organized in two independent groups, i.e. experimental group vs. controlled group for each other. Forty reviewers have completed the whole experiment and provided the data for statistical analysis on the effects of the approach. The experiment shows that the proposed method has significantly improved True Positives and Sensitivity by about 400%, improved Precision by approximately 200%, and reduced around one-third of False Positives. The effects were consistent across different tasks and different code reviewers.
Security Support on Memory Controller for Heap Memory Safety

Memory corruption attacks have existed for multiple decades, and have become a major threat to computer systems. At the same time, a number of defense techniques have been proposed by research community. With the wide adoption of CPU-based memory safety solutions, sophisticated attackers tend to tamper with system memory via direct memory access (DMA) attackers, which leverage DMA-enabled I/O peripherals to fully compromise system memory. The Input-Output Memory Management Units (IOMMUs) based solutions are widely believed to mitigate DMA attacks. However, recent works point out that attackers can bypass IOMMU-based protections by manipulating the DMA interfaces, which are particularly vulnerable to race conditions and other unsafe interactions.State-of-the-art hardware-supported memory protections rely on metadata to perform security checks on memory access. Consequently, the additional memory request for metadata results in significant performance degradation, which limited their feasibility in real world deployments. For quantitative analysis, we separate the total metadata access latency into DRAM latency, on-chip latency, and cache latency, and observe that the actual DRAM access is less than half of the total latency. To minimize metadata access latency, we propose EMC, a low-overhead heap memory safety solution that implements a tripwire based mechanism on the memory controller. In addition, by using memory controller as a natural gateway of various memory access data paths, EMC could provide comprehensive memory safety enforcement to all memory data paths from/to system physical memory. Our evaluation shows an 0.54% performance overhead on average for SPEC 2017 workloads.
SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection

Audio DeepFakes are utterances generated with the use of deep neural networks. They are highly misleading and pose a threat due to use in fake news, impersonation, or extortion. In this work, we focus on increasing accessibility to the audio DeepFake detection methods by providing SpecRNet, a neural network architecture characterized by a quick inference time and low computational requirements. Our benchmark shows that SpecRNet, requiring up to about 40% less time to process an audio sample, provides performance comparable to LCNN architecture — one of the best audio DeepFake detection models. Such a method can not only be used by online multimedia services to verify a large bulk of content uploaded daily but also, thanks to its low requirements, by average citizens to evaluate materials on their devices. In addition, we provide benchmarks in three unique settings that confirm the correctness of our model. They reflect scenarios of low–resource datasets, detection on short utterances and limited attacks benchmark in which we take a closer look at the influence of particular attacks on given architectures.
Eye-based keystroke prediction for natural texts – a feasibility analysis

The use of videoconferencing is on the rise after COVID-19, being common to look at the screen and see someone typing. A side-channel attack may be launched to infer the text written from the face image. In this paper, we analyse the feasibility of such an attack, being the first proposal which work with a complete keyset (50 keys) and natural texts. We use different scenarios, lighting conditions and natural texts to increase realism. Our study involves 30 participants, who typed 49,365 keystrokes. We characterize the effect of lighting, gender, age and use of glasses. Our results show that on average 13.71% of keystrokes are revealed without error, and up to 31.8%, 52.5% and 61.2% are guessed with a maximum error of 1, 2 and 3 keys, respectively.