Denial-of-Service Vulnerability of Hash-based Transaction Sharding: Attacks and Countermeasures

Since 2016, sharding has become an auspicious solution to tackle the scalability issue in legacy blockchain systems. Despite its potential to strongly boost the blockchain throughput, sharding comes with its own security issues. To ease the process of deciding which shard to place transactions, existing sharding protocols use a hash-based transaction sharding in which the hash value of a transaction determines its output shard. Unfortunately, we show that this mechanism opens up a loophole that could be exploited to conduct a single-shard flooding attack, a type of Denial-of-Service (DoS) attack, to overwhelm a single shard that ends up reducing the performance of the system as a whole. To counter the single-shard flooding attack, we propose a countermeasure that essentially eliminates the loophole by rejecting the use of hash-based transaction sharding. The countermeasure leverages the Trusted Execution Environment (TEE) to let blockchain's validators securely execute a transaction sharding algorithm with a negligible overhead. We provide a formal specification for the countermeasure and analyze its security properties in the Universal Composability (UC) framework. Finally, a proof-of-concept is developed to demonstrate the feasibility and practicality of our solution

NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee

Despite recent studies on understanding deep neural networks (DNNs), there exists numerous questions on how DNNs generate their predictions. Especially, given similar predictions on different input samples, are the underlying mechanisms generating those predictions the same? In this work, we propose NeuCEPT, a method to locally discover critical neurons that play a major role in the model's predictions and identify model's mechanisms in generating those predictions. We first formulate a critical neurons identification problem as maximizing a sequence of mutual-information objectives and provide a theoretical framework to efficiently solve for critical neurons while keeping the precision under control. NeuCEPT next heuristically learns different model's mechanisms in an unsupervised manner. Our experimental results show that neurons identified by NeuCEPT not only have strong influence on the model's predictions but also hold meaningful information about model's mechanisms.Comment: 6 main page

XRand: Differentially Private Defense against Explanation-Guided Attacks

Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query. However, XAI also opens a door for adversaries to gain insights into the black-box models in MLaaS, thereby making the models more vulnerable to several attacks. For example, feature-based explanations (e.g., SHAP) could expose the top important features that a black-box model focuses on. Such disclosure has been exploited to craft effective backdoor triggers against malware classifiers. To address this trade-off, we introduce a new concept of achieving local differential privacy (LDP) in the explanations, and from that we establish a defense, called XRand, against such attacks. We show that our mechanism restricts the information that the adversary can learn about the top important features, while maintaining the faithfulness of the explanations.Comment: To be published at AAAI 202

Catching Up to Move Forward: A Computer Science Education Landscape Report of Hawai‘i Public Schools, 2017–2020

A Computer Science Education Landscape Report of Hawai‘i Public Schools, 2017–2020This report is a computer science education landscape report and presents results of a study conducted by the Curriculum Research & Development Group in the College of Education at the University of Hawai‘i at Mānoa on behalf of the Hawai‘i Department of Education (HIDOE) in 2020. The purpose of the report is to examine the landscape of public school K–12 computer science education in Hawai‘i, particularly after the passing of Act 51 (HRS 302A-323). Results here are based on analysis of data from the Hawai‘i State Department of Education (HIDOE) and national data systems; data from a HIDOE survey of 492 K–12 educators and administrators; and 5 follow-up sets of interviews with educators, administrators, industry partners, and the state computer science education team. Key findings include the following: - a rapid increase of computer science activities between 2017 and 2020; - a total 33 public high schools and 11 combination schools offering computer science courses, which is 100% of high schools; - an increase of 89.6% for AP CS Principles and 28.7% for AP CS A from SY 2017–18 to SY 2018–19 exam takers; - an increase from 6.8% to 22.7% of Title I schools that offered AP CS courses from SY 2017–18 to SY 2019–20; - a need for a process of feedback and support for computer science education activities; - a high percentage of schools using programs like Code.org and Scratch; - minimal to no change in the proportion of participation by girls, Native Hawaiian students, and other underrepresented minorities in formal course enrollment; - an increase in girls’ participation in AP CS exam taking, but not in the overall proportion of CS course enrollment; - an increase in the presence of computer science opportunities in Title I schools; - a tension of time needed to implement computer science education and other initiatives; - a lack of incorporation of elements of the HĀ framework; and - a high number of ESSA highly-qualified teachers, but a low number of teachers licensed in computer science. The intent of the authors is to provide - a comparison of Hawai‘i to national computer science education trends; - a description of the current K–12 computer science opportunities in Hawai‘i public schools; - a broad report of the research results from survey, interview, and document data; and - a set of recommendations for addressing the local issues that this data uncovers. Recommendations include - maintaining continuity and sustainability of CS Initiatives; - creating additional subsidies for AP examinations; - establishing common language around computer science education; - developing pathways toward computer science college majors and careers; - creating effective supports for teachers; - rethinking traditional teaching models; and - committing to equity and access.Developed for the Hawai‘i Department of Education under MOA D20-111 CO-20089. The contents do not necessarily represent the policy of the Hawai‘i Department of Education and should not be viewed as endorsed by the state government

A Scalable Wi-Fi Based Localization Algorithm

This paper proposes an applicable and scalable approach which allows deploying the fingerprint Wi-Fi localization algorithm for different mobile devices. The original fingerprint localization algorithm performs accurately when the mobile device used in the deployment phase is the same as the mobile device used in the training phase. However, when a different mobile device is used in the deployment phase, a time-consuming re-training step (in the order of hours or days) is required to achieve the equivalent degree of accuracy. Our proposed approach replaces this re-training step by a short period of calibration (in the order of a few minutes), which can be done transparently to the user. To validate our approach, we did an analysis on collected data from a large scale experiment (14 laptops and 2 smartphones with 224-hour of collected data) and evaluated the performance on the real devices

Active Membership Inference Attack under Local Differential Privacy in Federated Learning

Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server. In this paper, we propose a new active membership inference (AMI) attack carried out by a dishonest server in FL. In AMI attacks, the server crafts and embeds malicious parameters into global models to effectively infer whether a target data sample is included in a client's private training data or not. By exploiting the correlation among data features through a non-linear decision boundary, AMI attacks with a certified guarantee of success can achieve severely high success rates under rigorous local differential privacy (LDP) protection; thereby exposing clients' training data to significant privacy risk. Theoretical and experimental results on several benchmark datasets show that adding sufficient privacy-preserving noise to prevent our attack would significantly damage FL's model utility.Comment: Published at AISTATS 202

Adverse drug reactions associated with amitriptyline - protocol for a systematic multiple-indication review and meta-analysis

Background: Unwanted anticholinergic effects are both underestimated and frequently overlooked. Failure to identify adverse drug reactions (ADRs) can lead to prescribing cascades and the unnecessary use of over-thecounter products. The objective of this systematic review and meta-analysis is to explore and quantify the frequency and severity of ADRs associated with amitriptyline vs. placebo in randomized controlled trials (RCTs) involving adults with any indication, as well as healthy individuals. Methods: A systematic search in six electronic databases, forward/backward searches, manual searches, and searches for Food and Drug Administration (FDA) and European Medicines Agency (EMA) approval studies, will be performed. Placebo-controlled RCTs evaluating amitriptyline in any dosage, regardless of indication and without restrictions on the time and language of publication, will be included, as will healthy individuals. Studies of topical amitriptyline, combination therapies, or including <100 participants, will be excluded. Two investigators will screen the studies independently, assess methodological quality, and extract data on design, population, intervention, and outcomes ((non-)anticholinergic ADRs, e.g., symptoms, test results, and adverse drug events (ADEs) such as falls). The primary outcome will be the frequency of anticholinergic ADRs as a binary outcome (absolute number of patients with/without anticholinergic ADRs) in amitriptyline vs. placebo groups. Anticholinergic ADRs will be defined by an experienced clinical pharmacologist, based on literature and data from Martindale: The Complete Drug Reference. Secondary outcomes will be frequency and severity of (non-)anticholinergic ADRs and ADEs. The information will be synthesized in meta-analyses and narratives. We intend to assess heterogeneity using metaregression (for indication, outcome, and time points) and I2 statistics. Binary outcomes will be expressed as odds ratios, and continuous outcomes as standardized mean differences. Effect measures will be provided using 95% confidence intervals. We plan sensitivity analyses to assess methodological quality, outcome reporting etc., and subgroup analyses on age, dosage, and duration of treatment. Discussion: We will quantify the frequency of anticholinergic and other ADRs/ADEs in adults taking amitriptyline for any indication by comparing rates for amitriptyline vs. placebo, hence, preventing bias from disease symptoms and nocebo effects. As no standardized instrument exists to measure it, our overall estimate of anticholinergic ADRs may have limitations