Generative artificial intelligence (AI) has become popular. The combination of increasingly complex datasets beyond human comprehension and the widespread availability of advanced computing systems—such as graphics processing unit (GPU) and tensor processing unit (TPU)—has driven the rapid advancement of generative AI. This technology has found applications in areas such as voice recognition, recommendation systems and data privacy preservation, which foster more data sharing and reuse. While challenges related to bias, fairness and uncertainty in AI continue to evolve, emerging government regulations aim to ensure ethical use and maximize societal benefits. In this paper, we present a system that leverages generative adversarial network (GAN) to enable privacy-preserving data publishing. The system supports contact tracing for infectious diseases like coronavirus disease 2019 (COVID-19) and monkey-pox. Evaluation using COVID-19 data highlights the practicality and effectiveness of our system.

Breast cancer is one of the most common cancers in women. Currently, mammography is one of the best methods for early detection of breast cancer. The rapid development of deep learning has attracted much attention from researchers in an effort to develop automatic support tools for breast cancer diagnosis. There are many published works on deep learning models supporting the diagnosis of breast cancer using mammography. However, most—if not all—deep learning models perform well on certain or similar datasets but fail with unknown datasets that come with different distribution. Thus, there is a critical need of deterministic models, which can work for any datasets. In this paper, we propose a novel approach to develop deterministic models, in which we emphasize the role of data mining as a prerequisite step that can help model detect and locate region of interest (ROIs) accurately. Furthermore, our approach can perform mammogram image segmentation and classification in parallel.

Evaluation of highly dependable systems requires estimating the probability of a significant rare event under which the system fails to meet the requirement. To improve the estimation accuracy, advanced Monte Carlo simulation techniques such as importance sampling (IS) are commonly used. However, IS is known to misbehave under high dimension. As a result, the IS estimator can have a large relative error and underestimate the rare event probability. In this paper, we propose a novel IS method based on the idea of maximum weight minimization (MWM). Our method works by finding the sampling distribution that minimizes the maximum weight of a rare event sample. To alleviate the curse of dimensionality, we develop further heuristics based on two problem-specific structures, namely, monotonicity and symmetry. Using extensive examples from network reliability, stochastic flow analysis, cyber-security risk assessment, and fault tree analysis, we evaluate the performance of MWM, demonstrate its accuracy and scalability, and highlight applications where it outperforms state-of-the-art techniques.

In this paper, we discuss an efficient and effective index mechanism for the string matching with k differences, by which we will find all the substrings of a target string y of length n that align with a pattern string x of length m with not more than k insertions, deletions, and mismatches. A typical application is the searching of a DNA database, where the size of a genome sequence in the database is much larger than that of a pattern. For example, n is often on the order of millions or billions while m is just a hundred or a thousand. The main idea of our method is to transform y to a BWT-array as an index, denoted as BWT ( y ), and search x against it. The time complexity of our method is bounded by O( k · | T |), where T is a tree structure dynamically generated during a search of BWT ( y ). The average value of | T | is bounded by O(|Σ| 2 k ), where Σ is an alphabet from which we take symbols to make up target and pattern strings. This time complexity is better than previous strategies when k ≤ O(log |Σ| n ). The general working process consists of two steps. In the first step, x is decomposed into a series of l small subpatterns, and BWT ( y ) is utilized to speedup the process to figure out all the occurrences of such subpatterns with ⌊ k/l ⌋ differences. In the second step, all the found occurrences in the first step will be rechecked to see whether they really match x , but with k differences. Extensive experiments have been conducted, which show that our method for this problem is promising.

Cyber-security risk assessment includes estimation of losses possible to a system due to cyber-attacks. As there are uncertain elements to this and as we model uncertainty using probability, we seek to estimate the attack loss distribution. In particular, the tail of the distribution represents the low-probability but high-impact events. However, quantifying those events using standard Monte Carlo techniques is inefficient due to the low probability. This paper proposes a novel cyber-security risk assessment approach based on uncertain graphs, with an emphasis on modeling losses due to cyber-attacks. Under rare event realizations where the attack loss is greater than a selected threshold, we (i) derive the analytically optimal importance sampling scheme for the loss tail probability and (ii) propose an approximation to the optimal importance sampling scheme which has the assurance of bounded relative error. While the approximation scheme requires solving an NP-hard problem, we use a search procedure that becomes more efficient as the attack loss threshold increases. A case study on a medium-sized network demonstrates the use and performance of our approach.

Network reliability studies properties of networks subjected to random failures of their components. It has been widely adopted to modeling and analyzing real-world problems across different domains, such as circuit design, genomics, databases, information propagation, network security, and many others. Two practical situations that usually arise from such problems are (i) the correlation between component failures and (ii) the uncertainty in failure probabilities. Previous work captured correlations by modeling component reliability using general Boolean expression of Bernoulli random variables. This paper extends such a model to address the second problem, where we investigate the use of Beta distributions to capture the variance of uncertainty. We call this new formalism the Beta uncertain graph. We study the reliability polynomials of Beta uncertain graphs as multivariate polynomials of Beta random variables and demonstrate the use of the model on two realistic examples. We also observe that the reliability distribution of a monotone Beta uncertain graph can be approximated by a Beta distribution, usually with high accuracy. Numerical results from Monte Carlo simulation of an approximation scheme and from two case studies strongly support this observation.

Load shedding can combat the overload of a power grid that may jeopardize the grid’s safety. However, disconnected customers may be excessively inconvenienced or even endangered. With the emergence of demand-response based on cyber-enabled smart meters and appliances, customers may participate in solving the overload by curtailing their demands collaboratively such that no single customers will have to bear a disproportionate burden of reduced usage. However, compliance or commitment to curtailment requests by untrusted users is uncertain, which causes an important safety concern. This article proposes a two-phase load management scheme that (i) gives customers a chance to curtail their demands and correct a grid’s overload when there are no immediate safety concerns but (ii) falls back to load shedding to ensure safety once the grid enters a vulnerable state. Extensive simulations based on a 37-bus electrical grid and traces of real electrical load demonstrate the effectiveness of this scheme. In particular, if customers are, as expected, sufficiently committed to the load curtailment, overloads can be resolved in real time by collaborative and graceful usage degradation among them, thereby avoiding unpleasant load shedding.

This paper studies the impact of false data injection (FDI) attacks on automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions, such as disconnecting customer loads or generators, leading to blackouts, and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of FDIs that minimizes the remaining time until the onset of disruptive remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of the FDIs on power grids, and provides an analysis framework to guide the protection of sensor data links. For countermeasures, we develop efficient algorithms to detect the attack, estimate which sensor data links are under attack, and mitigate attack impact. Our analysis and algorithms are validated by experiments on a physical 16-bus power system test bed and extensive simulations based on a 37-bus power system model.

Fine-grained real-time metering is a fundamental service of wireless energy auditing networks, where metering data is transmitted from embedded wireless power meters to gateways for centralized processing, storage, and forwarding. Due to limited meter capability and wireless bandwidth, the increasing sampling rates and network scales needed to support new energy auditing applications pose significant challenges to metering data fidelity and secrecy . This article exploits the compression and encryption properties of compressive sensing (CS) to design a joint data compression and encryption (JICE) approach that addresses these two challenges simultaneously. Compared with a conventional signal processing pipeline that compresses and encrypts data sequentially, JICE reduces computation and space complexities due to its simple design. It thus leaves more processor time and available buffer space for handling lossy wireless transmissions. Moreover, JICE features an adaptive reconfiguration mechanism that selects the signal representation basis of CS at runtime among several candidate bases to achieve the best fidelity of the recovered data at the gateways. This mechanism enables JICE to adapt to changing power consumption patterns. On a smart plug platform, we implemented JICE and several baseline approaches including downsampling, lossless compression, and the pipeline approach. Extensive testbed experiments show that JICE achieves higher data delivery ratios and lower recovery distortions under a range of realistic settings. In particular, at a meter sampling rate of 8 Hz, JICE increases the number of meters supported by a gateway by 50%, compared with the commonly used pipeline approach, while keeping a signal distortion rate lower than 5%.

Fine-grained real-time metering is a fundamental service of wireless energy auditing networks, where metering data is transmitted from embedded power meters to gateways for centralized processing, storage, and forwarding. Due to limited meter capability and wireless bandwidth, the increasing sampling rates and network scales needed to support new energy auditing applications pose significant challenges to metering data fidelity and secrecy. This paper exploits the compression and encryption properties of compressive sensing (CS) to design a joint data compression and encryption (JICE) approach that addresses these two challenges simultaneously. Compared with a conventional signal processing pipeline that compresses and encrypts data sequentially, JICE reduces computation and storage complexities due to its simple design. It thus leaves more processor time and available buffer space for handling lossy wireless transmissions. Moreover, JICE features a machine-learning-based reconfiguration mechanism that adapts its signal representation basis to changing power patterns autonomously. On a smart plug platform, we implemented JICE and several baseline approaches including downsampling, lossless compression, and the pipeline approach. Extensive testbed experiments show that JICE achieves higher data delivery ratios and lower recovery distortions under a range of realistic settings. In particular, JICE increases the number of meters supported by a gateway by 50%, compared with the pipeline approach, while keeping a distortion rate lower than 5%.

In this demonstration, we present a working prototype that uses an Anisotropic Magnetoresistive (AMR) sensor array to estimate the electricity usage on individual branches of an electricity panel. Our design enables the general public to retrofit an electricity panel: one simply needs to attach a compact AMR sensor array onto the panel. Our system can then exploit the inherent power patterns of electric loads to automatically infer the system parameters and estimate the per-branch currents accurately. Even for branches carrying small loads (e.g., a 30W fan), the estimation error of our system is below 10%.