Location Privacy in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. This chapter identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC-server). The latter phase is reporting, in which workers travel to the tasks' locations, complete the tasks and upload their reports to the SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks). This chapter aims to provide an overview of the state-of-the-art in protecting users' location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchange-based and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work

Differential privacy and game theory in cybersecurity

University of Technology Sydney. Faculty of Engineering and Information Technology.The vast majority of cybersecurity solutions are founded on game theory, and differential privacy is emerging as perhaps the most rigorous and widely-adopted privacy paradigm in the field. However, alongside all the advancements made in both fields, there is not a single application that is not still vulnerable to privacy violations, security breaches, or manipulation by adversaries. The current understanding of the interactions between differential privacy and game-theoretic solutions is limited. Hence, this thesis undertook a comprehensive exploration of differential privacy and game theory in the field of cybersecurity, finding that differential privacy has several advantageous properties that can make more of a contribution to game theory than just privacy protection. It can also be used to build heuristic game-theoretic models for cybersecurity solutions, to avert strategic manipulations by adversaries, and to quantify the cost of information leakage. With a focus on cybersecurity, the aim of this thesis is to provide new perspectives on the currently-held impossibilities in privacy and security issues, potential avenues to circumvent those impossibilities, and opportunities to improve the performance of cybersecurity solutions with game-theoretic and differentially private techniques

Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs

Machine unlearning enables pre-trained models to eliminate the effects of partial training samples. Previous research has mainly focused on proposing efficient unlearning strategies. However, the verification of machine unlearning, or in other words, how to guarantee that a sample has been successfully unlearned, has been overlooked for a long time. Existing verification schemes typically rely on machine learning attack techniques, such as backdoor or membership inference attacks. As these techniques are not formally designed for verification, they are easily bypassed when an untrustworthy MLaaS undergoes rapid fine-tuning to merely meet the verification conditions, rather than executing real unlearning. In this paper, we propose a formal verification scheme, IndirectVerify, to determine whether unlearning requests have been successfully executed. We design influential sample pairs: one referred to as trigger samples and the other as reaction samples. Users send unlearning requests regarding trigger samples and use reaction samples to verify if the unlearning operation has been successfully carried out. We propose a perturbation-based scheme to generate those influential sample pairs. The objective is to perturb only a small fraction of trigger samples, leading to the reclassification of reaction samples. This indirect influence will be used for our verification purposes. In contrast to existing schemes that employ the same samples for all processes, our scheme, IndirectVerify, provides enhanced robustness, making it less susceptible to bypassing processes

Towards Efficient Target-Level Machine Unlearning Based on Essential Graph

Machine unlearning is an emerging technology that has come to attract widespread attention. A number of factors, including regulations and laws, privacy, and usability concerns, have resulted in this need to allow a trained model to forget some of its training data. Existing studies of machine unlearning mainly focus on unlearning requests that forget a cluster of instances or all instances from one class. While these approaches are effective in removing instances, they do not scale to scenarios where partial targets within an instance need to be forgotten. For example, one would like to only unlearn a person from all instances that simultaneously contain the person and other targets. Directly migrating instance-level unlearning to target-level unlearning will reduce the performance of the model after the unlearning process, or fail to erase information completely. To address these concerns, we have proposed a more effective and efficient unlearning scheme that focuses on removing partial targets from the model, which we name "target unlearning". Specifically, we first construct an essential graph data structure to describe the relationships between all important parameters that are selected based on the model explanation method. After that, we simultaneously filter parameters that are also important for the remaining targets and use the pruning-based unlearning method, which is a simple but effective solution to remove information about the target that needs to be forgotten. Experiments with different training models on various datasets demonstrate the effectiveness of the proposed approach

Machine Unlearning: A Survey

Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more. Yet a special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. At the same time, this ambitious problem has led to numerous research efforts aimed at confronting its challenges. To the best of our knowledge, no study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. Accordingly, with this survey, we aim to capture the key concepts of unlearning techniques. The existing solutions are classified and summarized based on their characteristics within an up-to-date and comprehensive review of each category's advantages and limitations. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities

Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach

With the growing need to comply with privacy regulations and respond to user data deletion requests, integrating machine unlearning into IoT-based federated learning has become imperative. Traditional unlearning methods, however, often lack verifiable mechanisms, leading to challenges in establishing trust. This paper delves into the innovative integration of blockchain technology with federated learning to surmount these obstacles. Blockchain fortifies the unlearning process through its inherent qualities of immutability, transparency, and robust security. It facilitates verifiable certification, harmonizes security with privacy, and sustains system efficiency. We introduce a framework that melds blockchain with federated learning, thereby ensuring an immutable record of unlearning requests and actions. This strategy not only bolsters the trustworthiness and integrity of the federated learning model but also adeptly addresses efficiency and security challenges typical in IoT environments. Our key contributions encompass a certification mechanism for the unlearning process, the enhancement of data security and privacy, and the optimization of data management to ensure system responsiveness in IoT scenarios.Comment: 13 pages, 25 figure

Federated TrustChain: Blockchain-Enhanced LLM Training and Unlearning

The development of Large Language Models (LLMs) faces a significant challenge: the exhausting of publicly available fresh data. This is because training a LLM needs a large demanding of new data. Federated learning emerges as a promising solution, enabling collaborative model to contribute their private data to LLM global model. However, integrating federated learning with LLMs introduces new challenges, including the lack of transparency and the need for effective unlearning mechanisms. Transparency is essential to ensuring trust and fairness among participants, while accountability is crucial for deterring malicious behaviour and enabling corrective actions when necessary. To address these challenges, we propose a novel blockchain-based federated learning framework for LLMs that enhances transparency, accountability, and unlearning capabilities. Our framework leverages blockchain technology to create a tamper-proof record of each model's contributions and introduces an innovative unlearning function that seamlessly integrates with the federated learning mechanism. We investigate the impact of Low-Rank Adaptation (LoRA) hyperparameters on unlearning performance and integrate Hyperledger Fabric to ensure the security, transparency, and verifiability of the unlearning process. Through comprehensive experiments and analysis, we showcase the effectiveness of our proposed framework in achieving highly effective unlearning in LLMs trained using federated learning. Our findings highlight the feasibility of integrating blockchain technology into federated learning frameworks for LLMs.Comment: 16 pages, 7 figures

QUEEN: Query Unlearning against Model Extraction

Model extraction attacks currently pose a non-negligible threat to the security and privacy of deep learning models. By querying the model with a small dataset and usingthe query results as the ground-truth labels, an adversary can steal a piracy model with performance comparable to the original model. Two key issues that cause the threat are, on the one hand, accurate and unlimited queries can be obtained by the adversary; on the other hand, the adversary can aggregate the query results to train the model step by step. The existing defenses usually employ model watermarking or fingerprinting to protect the ownership. However, these methods cannot proactively prevent the violation from happening. To mitigate the threat, we propose QUEEN (QUEry unlEarNing) that proactively launches counterattacks on potential model extraction attacks from the very beginning. To limit the potential threat, QUEEN has sensitivity measurement and outputs perturbation that prevents the adversary from training a piracy model with high performance. In sensitivity measurement, QUEEN measures the single query sensitivity by its distance from the center of its cluster in the feature space. To reduce the learning accuracy of attacks, for the highly sensitive query batch, QUEEN applies query unlearning, which is implemented by gradient reverse to perturb the softmax output such that the piracy model will generate reverse gradients to worsen its performance unconsciously. Experiments show that QUEEN outperforms the state-of-the-art defenses against various model extraction attacks with a relatively low cost to the model accuracy. The artifact is publicly available at https://anonymous.4open.science/r/queen implementation-5408/

A clinical study on the application of three-dimensionally printed splints combined with finite element analysis in paediatric distal radius fractures

PurposeThis single-centre randomised clinical trial assessed the clinical efficacy and patient satisfaction of 3D-printed splints optimised via finite element analysis (FEA) for pediatric distal radius fractures.MethodsThis retrospective study included 56 children diagnosed with forearm fractures at our hospital between August 2023 and August 2024. Those who underwent traditional U-shaped forearm plaster immobilisation were compared with those who received a customised 3D-printed splint. FEA was conducted based on the biomechanical characteristics of the forearm; the splint structure was optimised based on the analysis results and created via 3D printing. Outcomes were evaluated using the Patient Satisfaction Questionnaire and Wong-Baker Faces Pain Scale–Revised. Forearm function was evaluated using the Mayo Wrist Score and radiological outcomes. A power calculation was not performed due to the exploratory scope and resource limitations of this preliminary study.ResultsThe treatment costs significantly differed between the two groups (p = 0.001). In the Patient Satisfaction Questionnaire, the hot and sweaty item showed no significant difference (p = 0.089), whereas the last week's comfort (p = 0.001), first applied comfort (p = 0.004), weight (p = 0.001), itchiness (p = 0.015), smell (p = 0.003), and overall satisfaction items significantly differed between the two groups (p = 0.004). A comparison of the Mayo Wrist Score showed a statistically significant difference between the two groups after external fixation removal (p = 0.044). There were no significant differences between the two groups in terms of the palmar tilt angle (p = 0.196), ulnar deviation angle (p = 0.460), or height of the radial styloid (p = 0.111).ConclusionBoth 3D-printed splint and plaster cast fixation methods can effectively treat distal radial fractures in children, but the 3D-printed splint showed superior patient acceptance

Medical Economic Consequences, Predictors, and Outcomes of Immediate Atrial Fibrillation Recurrence after Radiofrequency Ablation

Background and aims: Immediate recurrence (Im-Recurr), a type of atrial fibrillation (AF) recurrence occurring during the blanking period after radiofrequency catheter ablation (RFCA), has received little attention. Therefore, this study was aimed at exploring the clinical significance of Im-Recurr in patients with AF after RFCA. Methods: This study retrospectively included patients with AF who underwent RFCA at our center. Regression, propensity score matching (PSM), and survival curve analyses were conducted to investigate the effects of Im-Recurr on costs, hospitalization durations, AF recurrence rates, and predictors of Im-Recurr. Results: A total of 898 patients were included, among whom 128 developed Im-Recurr after RFCA. Multiple linear regression analysis revealed that Im-Recurr correlated with greater cost, hospitalization duration, and hospitalization duration after ablation. Logistic regression and PSM analyses indicated that intraoperative electric cardioversion (IEC) was an independent predictor of Im-Recurr. The follow-up results suggested a significantly higher 1-year cumulative AF recurrence rate in the Im-Recurr group than the control group. Conclusions: Im-Recurr significantly increases the cost and length of hospitalization for patients with AF undergoing RFCA and is associated with an elevated 1-year cumulative AF recurrence rate. IEC serves as an independent predictor of Im-Recurr. Registration number: ChiCTR2200065235