Search for: All records

Background While genomic variations can provide valuable information for health care and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (eg, data hosts and hospitals) and that the query results are learned only by the user or authorized party. Objective In this study, we provide efficient and secure computations on panels of single nucleotide polymorphisms (SNPs) from genomic sequences as computed under the following set operations: union, intersection, set difference, and symmetric difference. Methods Using these operations, we can compute similarity metrics, such as the Jaccard similarity, which could allow querying a DNA database to find the same person and genetic relatives securely. We analyzed various security paradigms and show metrics for the protocols under several security assumptions, such as semihonest, malicious with honest majority, and malicious with a malicious majority. Results We show that our methods can be used practically on realistically sized data. Specifically, we can compute the Jaccard similarity of two genomes when considering sets of SNPs, each with 400,000 SNPs, in 2.16 seconds with the assumption of a malicious adversary in an honest majority and 0.36 seconds under a semihonest model. Conclusions Our methods may help adopt trusted environments for hosting individual genomic data with end-to-end data security. 

As deep-learning based image and video manipulation technology advances, the future of truth and information looks bleak. In particular, Deepfakes, wherein a person’s face can be transferred onto the face of someone else, pose a serious threat for potential spread of convincing misinformation that is drastic and ubiquitous enough to have catastrophic real-world consequences. To prevent this, an effective detection tool for manipulated media is needed. However, the detector cannot just be good, it has to evolve with the technology to keep pace with or even outpace the enemy. At the same time, it must defend against different attack types to which deep learning systems are vulnerable. To that end, in this paper, we review various methods of both attack and defense on AI systems, as well as modes of evolution for such a system. Then, we put forward a potential system that combines the latest technologies in multiple areas as well as several novel ideas to create a detection algorithm that is robust against many attacks and can learn over time with unprecedented effectiveness and efficiency. 

Risk patterns are crucial in biomedical research and have served as an important factor in precision health and disease prevention. Despite recent development in parallel and high-performance computing, existing risk pattern mining methods still struggle with problems caused by large-scale datasets, such as redundant candidate generation, inability to discover long significant patterns, and prolonged post pattern filtering. In this article, we propose a novel dynamic tree structure, Risk Hierarchical Pattern Tree (RHPTree), and a top-down search method, RHPSearch, which are capable of efficiently analyzing a large volume of data and overcoming the limitations of previous works. The dynamic nature of the RHPTree avoids costly tree reconstruction for the iterative search process and dataset updates. We also introduce two specialized search methods, the extended target search (RHPSearch-TS) and the parallel search approach (RHPSearch-SD), to further speed up the retrieval of certain items of interest. Experiments on both UCI machine learning datasets and sampled datasets of the Simons Foundation Autism Research Initiative (SFARI)—Simon’s Simplex Collection (SSC) datasets demonstrate that our method is not only faster but also more effective in identifying comprehensive long risk patterns than existing works. Moreover, the proposed new tree structure is generic and applicable to other pattern mining problems. 

With the advances in autonomous vehicles and intelligent intersection management systems, traffic lights may be replaced by optimal travel plans calculated for each passing vehicle in the future. While these technological advancements are envisioned to greatly improve travel efficiency, they are still facing various challenging security hurdles since even a single deviation of a vehicle from its assigned travel plan could cause a serious accident if the surrounding vehicles do not take necessary actions in a timely manner. In this paper, we propose a novel security mechanism namely NWADE which can be integrated into existing autonomous intersection management systems to help detect malicious vehicle behavior and generate evacuation plans. In the NWADE mechanism, we introduce the neighborhood watch concept whereby each vehicle around the intersection will serve as a watcher to report or verify the abnormal behavior of any nearby vehicle and the intersection manager. We propose a blockchainbased verification framework to guarantee the integrity and trustworthiness of the individual travel plans optimized for the entire intersection. We have conducted extensive experimental studies on various traffic scenarios, and the experimental results demonstrate the practicality, effectiveness, and efficiency of our mechanism. 

Anonymous communication, that is secure end-to-end and unlinkable, plays a critical role in protecting user privacy by preventing service providers from using message metadata to discover communication links between any two users. Techniques, such as Mix-net, DC-net, time delay, cover traffic, Secure Multiparty Computation (SMC) and Private Information Retrieval, can be used to achieve anonymous communication. SMC-based approach generally offers stronger simulation based security guarantee. In this paper, we propose a simple and novel SMC approach to establishing anonymous communication, easily implementable with two non-colluding servers which have only communication and storage related capabilities. Our approach offers stronger security guarantee against malicious adversaries without incurring a great deal of extra computation. To show its practicality, we implemented our solutions using Chameleon Cloud to simulate the interactions among a million users, and extensive simulations were conducted to show message latency with various group sizes. Our approach is efficient for smaller group sizes and sub-group communication while preserving message integrity. Also, it does not have the message collision problem. 

Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.