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In cloud computing scenarios, it is a common approach to encrypt the data before uploading in order to maintain the privacy. Public key encryption with equality test (PKEET) provides a generic solution to equality comparisons on encrypted data. Considering the practical requirements of PKEET in group user scenarios, Ling et al. presented a group public key encryption scheme with equality test (G-PKEET) by utilizing the computationally expensive bilinear pairing operations. In this paper, we propose an efficient and verifiable group public key encryption with equality test construction without bilinear pairings (GPKEET/BP). Our proposal is developed from a basic observation that two points determine a straight line, and is proven to be indistinguishable against the chosen-ciphertext attacks (IND-CCA) in the random oracle model under the computational Diffie-Hellman (CDH) assumption. Theoretical analysis shows that our construction only involves several modular exponentiations in the encryption, decryption and test algorithms. The theoretical results are well supported by experimental simulations, which show that the test algorithm of GPKEET/BP performs about 92 % faster than that of Ling et al.'s scheme. So our GPKEET/BP scheme turns out to be better suited for group ciphertext equality test situations. [ABSTRACT FROM AUTHOR]

Untreated mental illness in the Hoosier state comes at a cost of more than $4 billion a year, according to a new Indiana University study, the Daily Journal reported Nov. 1. The research published by the IU Richard M. Fairbanks School of Public Health found that one in five Indiana residents with mental illness do not receive the treatment they need. Hoosiers who do not receive such treatment are also more likely to experience other chronic health conditions, such as diabetes and cardiovascular disease, researchers said. The economic burden of untreated mental illness in Indiana is estimated to be $4.2 billion annually, including $3.3 billion in indirect costs — like unemployment and caregiving — $708.5 million in direct health care costs, and $185.4 million in non‐health care costs. The largest cost attributable to untreated mental illness was premature mortality, at over $1.4 billion. Productivity losses were estimated to cost $885 million each year. "The findings were published Oct. 13 in the Journal of the American Medical Association. Researchers worked with the Indiana Behavioral Health Commission to perform their analysis. Through the researchers' work, they developed a framework that allows Indiana to prioritize key areas in mental health services and treatment. The framework also provides Indiana with a baseline for tracking progress toward improvement efforts. The research was used in support of Senate Enrolled Act 1, which passed during the 2023 legislative session. The sweeping legislation will create a new mental health care system in Indiana, fortifying the relatively new 988 crisis response center and hotline with funding for mental health emergencies. The IU study population consisted of more than 6.1 million individuals of whom an estimated 429,000 had untreated mental illness in 2019, according to the research paper. "One of the most significant impacts of this research is that other states can use this framework to understand the financial burden in their state," said Justin Blackburn, Ph.D., associate professor at the Fairbanks School. "There is a scarcity of data on the costs incurred by each state — especially by individuals, families and communities — from untreated mental illnesses in the United States. Policymakers, clinicians and employers need this sort of data to determine how we should allocate our societal resources." [ABSTRACT FROM AUTHOR]

In hospital information systems, large volumes of electronic diagnostic records (EDRs) take up most of the storage space. While cloud server providers can reduce the local storage burden on hospitals and provide data-sharing services, the potential threat of sensitive data leakage and non-traceability of diagnoses prevents hospitals from uploading patients' diagnostic records to remote cloud servers directly. Therefore, to address security and traceability issues, we propose a new construction of post-quantum secure signcryption scheme with a designated equality test based on lattices (LB-SCDET) in this paper. Our LB-SCDET scheme allows a designated tester to perform equality tests on signcrypt-ciphertexts of EDRs without leaking what the EDRs actually are. Compared to the recent LB-SCET scheme proposed by Le et al., our LB-SCDET scheme implements a designated mechanism and is secure against offline message recovery attacks (OMRA). The comparison shows that our scheme enjoys a higher level of security, albeit the ciphertext size is slightly larger. Finally, we prove the scheme to be secure under the hardness of the Learning-with-Errors problem and unidirectionality of the Short-Integer-Solution problem. To be of independent interest, we show that the LB-SCET scheme fails to achieve the claimed IND-CCA security. • The LB-SCDET scheme supports the optional choice of a trusted tester for users. • The proposed scheme is proved secure under quantum computing attacks based on the LWE and SIS assumptions. • The proposed scheme is proved in standard model both OW-CCA and IND-CCA secure. • The LB-SCDET scheme is able to resist offline message recovery attacks, meanwhile resist inside attacks. [ABSTRACT FROM AUTHOR]

• Investigating COVID-19 spread patterns in different geospatial scales and discovering its transmission paths. • Generalizing the concept of neighborhood in cellular automata using dynamic weighted multigraph to support remote configuration transmission with dynamic control. • The capability of simulation and risk analysis of movement restrictions for measuring the impact of health and safety protocols during virus spread. • Implementing the proposed model and simulation of COVID-19 outbreak in five areas including three states Colorado, Indiana and Utah in USA as well as two countries USA and Iran. The pattern of coronavirus spread at different geographical scales verifies that travel or shipment by air, sea or road are potential to transmit viruses from one location to somewhere far away in a very short time. Simulation and analysis of such a situation requires the development of models that support long distance transmission of viruses. Cellular Automata (CA) are a family of spatiotemporal computational models frequently employed in analysis of biomedical systems. A CA consists of a topological combination of units called cells as well as a transition function that propagates the configuration of cells locally and step by step. In this paper, we first present some patterns that show the local interaction between CA cells is not sufficient for virus spread modeling, especially at large spatial scales. Then, we generalize the concept of CA by providing a symbiosis between the neighborhood relationship of cells and the transmission channels represented by a dynamic weighted multigraph. Furthermore, we characterize the capabilities of the proposed modeling tool in simulation of the virus spread, and estimating the risk control during the movement restrictions and related health protocols. Finally, we simulate the coronavirus outbreak in the five study areas including three states and two countries. Our experiments using the proposed model verify that the proposed model is capable of formulating different ways of virus transmission, including long-distance transmission, and supports high-precision simulation of the pandemic. [ABSTRACT FROM AUTHOR]