Overview of Key Agreement Protocols

The emphasis of this paper is to focus on key agreement. To this aim, we address a self-contained, up-to-date presentation of key agreement protocols at high level. We have attempted to provide a brief but fairly complete survey of all these schemes

Adaptively Secure Functional Encryption for Finite Languages from DLIN Assumption

In this paper, we present Functional Encryption (FE) schemes for finite languages from standard static assumption, viz., \textit{Decisional Linear} (DLIN) assumption. These finite languages are described by Deterministic Finite Automatas (DFAs). Our first scheme is ciphertext-policy functional encryption (CP-FE), where a key \sk_w is labeled with a string $w$ over a fixed alphabet $\Sigma$ and a ciphertext \cipher_\amn is associated with a DFA \amn over the same alphabet $\Sigma$. The key \sk_w can extract the message from the ciphertext \cipher_\amn if the DFA \amn accepts the string $w$. This CP-FE scheme is constructed based on attribute-based encryption (ABE) structure of Okamoto-Takashima in Asiacrypt, 2012. To achieve the adaptive security, we put bounds on number of occurrences of any symbol in a string and in the set of transition tuples of a DFA. Due to this restriction, the size of key space (where the keys are indexed with strings) is reduced to finite. Hence, the functional scope of any DFA in our system can capture only finite language. Similarly, we obtain our second adaptively secure FE scheme in key-policy flavor from DLIN assumption. Both the schemes are shown to be secure in the standard model

Factors affecting death due to COVID-19: an analytical study from a tertiary care hospital of Assam

Background: Globally, the Case Fatality Rate (CFR) due CoVID19 ranges from 0.1-4.7%. CoVID-19 death remained 0.5% till April 2021 in Assam as compared to India (1.3%). Though pre-existing diseases greatly contributes to CFR yet its association study from India is scarce. This study documents association of such death with comorbidities in a tertiary hospital of Assam. Methods: Retrospective analysis of 234 COVID death from May 2020 to December 2020 in Jorhat Medical College (JMCH) were done. Demography, comorbidities at admission and blood parameters were analyzed in Epi-Info version7.2.4.0. Continuous variables were presented as mean±SD or median (interquartile ranges) and correlated with death. Results: Out of 3781 confirmed cases admitted in JMCH, 234 died (72% male) with CFR of 0.06. Highest deaths occurred between 61 to 70 years. Median duration of disease was 4 days (IQR 2-8days). Acute respiratory distress or pneumonia was most common (53.1%) symptom followed by septicemia (24.6%) at admission. Diabetes mellitus (36.6%), hypertension (24.8%), diabetes with hypertension (11.4%) and chronic kidney diseases (22.4%) were common chronic comorbidities. About 64% cases had thrombocytopenia, and 66.9% had leukocytosis at admission. Many cases had coronary artery diseases, left ventricular failure, post-operative complications, post-partum complications, severe hemoptysis, severe anemia, metabolic encephalopathy, acute myocardial infarction, non-ketotic coma and acute gastroenteritis and SARS-COV2 infection. Conclusions: CoVID19 associated mortality in Assam was low and mostly among elderly with chronic comorbidities. CKD was most significantly associated with mortality. Superimposed bacterial infection at admission contributed to many fatal outcomes in COVID19, thus warranting proper empirical antibiotic

A Public Key Encryption In Standard Model Using Cramer-Shoup Paradigm

We present a public-key encryption scheme which is provably secure against adaptive chosen ciphertext attack. The scheme is constructed using Cramer-Shoup paradigm. The security of the scheme is based on the Decisional Bilinear Diffie-Hellman proble

Pairing-Based Cryptographic Protocols : A Survey

The bilinear pairing such as Weil pairing or Tate pairing on elliptic and hyperelliptic curves have recently been found applications in design of cryptographic protocols. In this survey, we have tried to cover different cryptographic protocols based on bilinear pairings which possess, to the best of our knowledge, proper security proofs in the existing security models

Training and Development Practice of Banks from an International Perspective with Special Emphasis on Bangladesh: Findings from the Literatures

The review is a collection of findings of journal articles. This descriptive work is based on training and development in banking industry. The banks are growing concerns and technologies, systems, processes, and procedures are changing here and consequently the employees need to know how and adapt to a new environment or situation for why they are supposed to go through the training and development processes. The review is aimed at extracting the findings from various journal articles from international perspective with special focus on Bangladeshi literatures. The findings have encrypted by naming and describing the findings in the journal articles under review. Lastly, the summarized discussion highlighted the possible suggestions to the banks emphasizing the importance of training and development in the industry. Keywords: Training and Development, Commercial Banks, Banglades

Attribute-Based Signcryption : Signer Privacy, Strong Unforgeability and IND-CCA2 Security in Adaptive-Predicates Attack

An Attribute-Based Signcryption (ABSC) is a natural extension of Attribute-Based Encryption (ABE) and Attribute-Based Signature (ABS), where we have the message confidentiality and authenticity together. Since the signer privacy is captured in security of ABS, it is quite natural to expect that the signer privacy will also be preserved in ABSC. In this paper, first we propose an ABSC scheme which is \textit{weak existential unforgeable, IND-CCA2} secure in \textit{adaptive-predicates} attack and achieves \textit{signer privacy}. Secondly, by applying strongly unforgeable one-time signature (OTS), the above scheme is lifted to an ABSC scheme to attain \textit{strong existential unforgeability} in \textit{adaptive-predicates} model. Both the ABSC schemes are constructed on common setup, i.e the public parameters and key are same for both the encryption and signature modules. Our first construction is in the flavor of $\mathcal{C}{t}\mathcal{E}\&\mathcal{S}$ paradigm, except one extra component that will be computed using both signature components and ciphertext components. The second proposed construction follows a new paradigm (extension of $\mathcal{C}{t}\mathcal{E}\&\mathcal{S}$), we call it ``Commit then Encrypt and Sign then Sign ($\mathcal{C}{t}\mathcal{E}\&\mathcal{S}{t}\mathcal{S}$). The last signature is done using a strong OTS scheme. Since the non-repudiation is achieved by $\mathcal{C}{t}\mathcal{E}\&\mathcal{S}$ paradigm, our systems also achieve the same

Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review

Background: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. Various Machine Learning (ML), Deep Learning (DL) and NLP techniques are studied and compared to understand the limitations and opportunities in this space comprehensively. Methodology: After screening 261 articles from 11 databases, we included 127 papers for full-text review covering seven categories of articles: 1) medical note classification, 2) clinical entity recognition, 3) text summarisation, 4) deep learning (DL) and transfer learning architecture, 5) information extraction, 6) Medical language translation and 7) other NLP applications. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Result and Discussion: EHR was the most commonly used data type among the selected articles, and the datasets were primarily unstructured. Various ML and DL methods were used, with prediction or classification being the most common application of ML or DL. The most common use cases were: the International Classification of Diseases, Ninth Revision (ICD-9) classification, clinical note analysis, and named entity recognition (NER) for clinical descriptions and research on psychiatric disorders. Conclusion: We find that the adopted ML models were not adequately assessed. In addition, the data imbalance problem is quite important, yet we must find techniques to address this underlining problem. Future studies should address key limitations in studies, primarily identifying Lupus Nephritis, Suicide Attempts, perinatal self-harmed and ICD-9 classification

A novel framework for distress detection through an automated speech processing system

Based on our ongoing work, this work in progress project aims to develop an automated system to detect distress in people to enable early referral for interventions to target anxiety and depression, to mitigate suicidal ideation and to improve adherence to treatment. The project will utilize either use existing voice data to assess people into various scales of distress, or will collect voice data as per existing standards of distress measurement, to develop basic computing algorithms required to detect various attributes associated with distress, detected through a person’s voice in a telephone call to a helpline. This will be then matched with the already available psychological assessment instruments such as the Distress Thermometer for these persons. In order to trigger interventions, organizational contexts are essential as interventions rely on the type of distress. Therefore, the model will be tested on various organizational settings such as the Police, Emergency and Health along with the Distress detection instruments normally used in a psychological assessment for accuracy and validation. The outcome of the project will culminate in a fully automated integrated system, and will save significant resources to organizations. The translation of the project will be realized in step-change improvements to quality of life within the gamut of public policy