A Novel Privacy Paradigm for Improving Serial Data Privacy.

Protecting the privacy of individuals is of utmost concern in today's society, as inscribed and governed by the prevailing privacy laws, such as GDPR. In serial data, bits of data are continuously released, but their combined effect may result in a privacy breach in the whole serial publication. Protecting serial data is crucial for preserving them from adversaries. Previous approaches provide privacy for relational data and serial data, but many loopholes exist when dealing with multiple sensitive values. We address these problems by introducing a novel privacy approach that limits the risk of privacy disclosure in republication and gives better privacy with much lower perturbation rates. Existing techniques provide a strong privacy guarantee against attacks on data privacy; however, in serial publication, the chances of attack still exist due to the continuous addition and deletion of data. In serial data, proper countermeasures for tackling attacks such as correlation attacks have not been taken, due to which serial publication is still at risk. Moreover, protecting privacy is a significant task due to the critical absence of sensitive values while dealing with multiple sensitive values. Due to this critical absence, signatures change in every release, which is a reason for attacks. In this paper, we introduce a novel approach in order to counter the composition attack and the transitive composition attack and we prove that the proposed approach is better than the existing state-of-the-art techniques. Our paper establishes the result with a systematic examination of the republication dilemma. Finally, we evaluate our work using benchmark datasets, and the results show the efficacy of the proposed technique.

MobChain: Three-Way Collusion Resistance in Witness-Oriented Location Proof Systems Using Distributed Consensus.

Smart devices have accentuated the importance of geolocation information. Geolocation identification using smart devices has paved the path for incentive-based location-based services (LBS). However, a user's full control over a smart device can allow tampering of the location proof. Witness-oriented location proof systems (LPS) have emerged to resist the generation of false proofs and mitigate collusion attacks. However, witness-oriented LPS are still susceptible to three-way collusion attacks (involving the user, location authority, and the witness). To overcome the threat of three-way collusion in existing schemes, we introduce a decentralized consensus protocol called MobChain in this paper. In this scheme the selection of a witness and location authority is achieved through a distributed consensus of nodes in an underlying P2P network that establishes a private blockchain. The persistent provenance data over the blockchain provides strong security guarantees; as a result, the forging and manipulation of location becomes impractical. MobChain provides secure location provenance architecture, relying on decentralized decision making for the selection of participants of the protocol thereby addressing the three-way collusion problem. Our prototype implementation and comparison with the state-of-the-art solutions show that MobChain is computationally efficient and highly available while improving the security of LPS.

F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes.

With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches.

Navigating the Diagnosis and Treatment of Astroblastoma: A Pediatric Case Report.

Astroblastoma, a rare glial tumor of the central nervous system, presents diagnostic and therapeutic challenges due to its low incidence and variable clinical presentations. In this case study, we present the case of an 11-year-old boy with high-grade astroblastoma, highlighting the complexities in diagnosis and treatment. The clinical presentation initially involved right-sided motor weakness, which, after undergoing a brain MRI, revealed a large solid cystic mass in the left parietal lobe. Histopathological examination after undergoing surgery confirmed an astroblastoma with high-grade features, characterized by increased cellularity and high mitotic activity. Immunostaining patterns supported the glial origin of the tumor. Gross total resection remains the primary approach for its treatment, but adjuvant therapies for high-grade astroblastomas are still evolving, offering potential life-changing possibilities for the future. Due to its rarity, collecting sufficient data to develop an effective treatment protocol for this uncommon tumor is very challenging. This case underscores the importance of combined efforts and ongoing research to effectively navigate the diagnosis and treatment of astroblastoma.

The Prrx1eGFP Mouse Labels the Periosteum During Development and a Subpopulation of Osteogenic Periosteal Cells in the Adult.

The identity of the cells that form the periosteum during development is controversial with current dogma suggesting these are derived from a Sox9-positive progenitor. Herein, we characterize a newly created Prrx1eGFP reporter transgenic mouse line during limb formation and postnatally. Interestingly, in the embryo Prrx1eGFP-labeled cells become restricted around the Sox9-positive cartilage anlage without themselves becoming Sox9-positive. In the adult, the Prrx1eGFP transgene live labels a subpopulation of cells within the periosteum that are enriched at specific sites, and this population is diminished in aged mice. The green fluorescent protein (GFP)-labeled subpopulation can be isolated using fluorescence-activated cell sorting (FACS) and represents approximately 8% of all isolated periosteal cells. The GFP-labeled subpopulation is significantly more osteogenic than unlabeled, GFP-negative periosteal cells. In addition, the osteogenic and chondrogenic capacity of periosteal cells in vitro can be extended with the addition of fibroblast growth factor (FGF) to the expansion media. We provide evidence to suggest that osteoblasts contributing to cortical bone formation in the embryo originate from Prrx1eGFP-positive cells within the perichondrium, which possibly piggyback on invading vascular cells and secrete new bone matrix. In summary, the Prrx1eGFP mouse is a powerful tool to visualize and isolate periosteal cells and to quantify their properties in the embryo and adult. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Sphenoclival Intraosseous Lipoma: A Typical Lesion at an Atypical Location.

In this report, we present the case of a rare tumor in the sphenoclival region and discuss the potential pitfalls in its diagnosis and management. Intraosseous lipoma is a rare benign tumor, mostly accounting for 0.1% of all bone tumors. The disease is usually asymptomatic and mainly involves the hips, vertebrae, ribs, and metaphysis of the long bones. However, the intraosseous lipoma of the skull is less common, especially with few cases having been reported to involve the sphenoid bone in the literature. We present a rare case of sphenoclival intraosseous lipoma in a 28-year-old female who presented with a history of chronic headache. A non-contrast computed tomography (NCCT) was ordered, which revealed a deviated nasal septum with thickening of bilateral ethmoidal sinuses with mastoiditis and a well-defined fat-containing intraosseous lesion in the clivus with a mean HU~ of -32 with few septations within. The risk of malignant transformation in intraosseous lipoma is very low. The differential diagnosis of intraosseous lipoma includes end stage of infection, infarct lesions, intraosseous meningioma, angiolipoma, and myxofibrous tumors.

A strong construction of S-box using Mandelbrot set an image encryption scheme.

The substitution box (S-box) plays a vital role in creating confusion during the encryption process of digital data. The quality of encryption schemes depends upon the S-box. There have been several attempts to enhance the quality of the S-box by using fractal chaotic mechanisms. However, there is still weakness in the robustness against cryptanalysis of fractal-based S-boxes. Due to their chaotic behavior, fractals are frequently employed to achieve randomness by confusion and diffusion process. A complex number-based S-box and a chaotic map diffusion are proposed to achieve high nonlinearity and low correlation. This study proposed a Mandelbrot set S-box construction based on the complex number and Chen chaotic map for resisting cryptanalytic attacks by creating diffusion in our proposed algorithm. The cryptosystem was built on the idea of substitution permutation networks (SPN). The complex nature of the proposed S-box makes it more random than other chaotic maps. The robustness of the proposed system was analyzed by different analysis properties of the S-box, such as nonlinearity, strict avalanche criterion, Bit independent criterion, and differential and linear probability. Moreover, to check the strength of the proposed S-box against differential and brute force attacks, we performed image encryption with the proposed S-box. The security analysis was performed, including statistical attack analysis and NIST analysis. The analysis results show that the proposed system achieves high-security standards than existing schemes.

A Deep Learning-Based Privacy-Preserving Model for Smart Healthcare in Internet of Medical Things Using Fog Computing.

With the emergence of COVID-19, smart healthcare, the Internet of Medical Things, and big data-driven medical applications have become even more important. The biomedical data produced is highly confidential and private. Unfortunately, conventional health systems cannot support such a colossal amount of biomedical data. Hence, data is typically stored and shared through the cloud. The shared data is then used for different purposes, such as research and discovery of unprecedented facts. Typically, biomedical data appear in textual form (e.g., test reports, prescriptions, and diagnosis). Unfortunately, such data is prone to several security threats and attacks, for example, privacy and confidentiality breach. Although significant progress has been made on securing biomedical data, most existing approaches yield long delays and cannot accommodate real-time responses. This paper proposes a novel fog-enabled privacy-preserving model called δ r sanitizer, which uses deep learning to improve the healthcare system. The proposed model is based on a Convolutional Neural Network with Bidirectional-LSTM and effectively performs Medical Entity Recognition. The experimental results show that δ r sanitizer outperforms the state-of-the-art models with 91.14% recall, 92.63% in precision, and 92% F1-score. The sanitization model shows 28.77% improved utility preservation as compared to the state-of-the-art.

Beaver Tail Liver: A Hepatic Morphology Variant.

Beaver tail liver, or else known as the sliver of liver, is a rare anatomic variation of the liver where the left lobe of the liver extends laterally to contact and enwrap the spleen. A case is presented here where a middle-aged male presented with complaints of abdominal pain, hematuria, and fever. After the routine blood and urine examinations revealed urinary tract infection, CT abdomen was done to find out the etiology, and beaver tail liver was found incidentally with the left lobe of liver encircling the spleen. Sometimes it may be difficult to differentiate liver and spleen from each other when echogenicity or density on USG and CT are equivalent. More common in females, it may imitate a splenic trauma or a subcapsular hematoma, or a perisplenic hemorrhage within the splenic parenchyma.

Von Hippel-Lindau Disease: A Rare Radiological Case Report of a Symptomatic Patient and His Asymptomatic Genetic Counterpart.

Von Hippel-Lindau (VHL) disease is an inherited syndrome manifested as a benign and malignant tumor. It is an autosomal dominant syndrome diagnosed approximately in 1 in 36,000 people. We report a case where male siblings presented with the involvement of bilateral kidneys and the multi-cystic lesion on the pancreas in both. Reverse transcription polymerase chain reaction (RT-PCR) was conducted to detect the VHL gene, which turned out to be a significant finding in our study. The rare involvement of both pancreas and kidneys was noted in the siblings with VHL in the present study. In patients with VHL-associated tumour presentations, the most frequent detection of pathogenic variants in the VHL gene is the result of directed genetic testing or inherited cancer gene panels. The presence of renal and pancreatic involvement is rare but a significant finding present within the family member who needs to be screened.

Spectrum of Chest Dual-Energy Computed Tomography Findings in COVID Patients in North India.

Purpose To study the spectrum of chest dual-energy computed tomography (DECT) imaging findings in severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) or COVID-19 infected Indian patients and classify them on the basis of the Radiological Society of North America CT classification. Method A total of 110 reverse transcription-polymerase chain reaction (RT-PCR)-positive patients (subjects) in which noncontrast chest DECT was done in our COVID-19 care center (CCC) were enrolled in this study. The prevalence of various abnormalities of lung parenchyma due to SARS-COV-2 and their distribution with extent was recorded. Various types of lung parenchyma abnormalities due to COVID-19 were evaluated in all patients. Data were analyzed and various prevalent abnormalities were calculated as a percentage for each type. All the cases were also sorted into four major groups on the basis of the Radiological Society of North America CT classification of COVID patients. Result Among the total 110 patients that were enrolled in this study, 80 (72.7%) were males and 30 (27.3%) were females with a mean age of 40.5 ± 7 years (range 24-84). Out of this, we observed that 59 (53.6%) cases had abnormalities of lung parenchyma and were designated as DECT positive, whereas 51 (46.3%) cases had completely normal DECT. Only 14 (12.7%) of the patients (cases) presented with dyspnoea, 10 (9%) had hyperpnoea, whereas 12 (10.8%) had other associated comorbidities. Among the patients having abnormal DECT findings, multilobar (86%), bilateral lung field involvement (72.8%) with the ascendancy of peripheral and posterior distribution was most commonly noted. With respect to the different types of opacities noted in various patients, we found that ground-glass opacity (GGO) was the common abnormality found in almost all cases for the greatest part. Pure GGO was reported in 16 (28%), GGO admixed with a crazy-paving pattern were elicited in 17 (28.8%) and GGO mixed with consolidation was noted in 25 (42.3%) cases. Thirty-eight (64.4%) cases were having peri-lesional or intra-lesional segments or involving a small segment enlargement of the pulmonary vessel. Among the cases showing DECT positivity, the typical pattern on the basis of the Radiological Society of North America (RSNA) classification was noted in 71.2% of cases, whereas the atypical pattern was found in 1.2% percent of cases and the intermediate type was depicted in 25.4% percent of cases. Forty-six point three percent (46.3%) of the total cases that were enrolled in the study were grouped as the no pneumonia category. Conclusion The result of this study proved that the maximum number of RT-PCR-positive COVID-19 patients had mild symptoms and few comorbidities with normal chest DECT and fell under the no pneumonia category of the RSNA CT classification of COVID patients. However, out of the remaining patients, the majority of patients had GGO on DECT as a typical finding mixed with other patterns in a bilateral distribution and peripheral predominance. A preponderance of patients presented with the typical appearance of pneumonia followed by an intermediate type.

Establishment of Cephalic Index Using Cranial Parameters by Computed Tomography in a Sampled North Indian Population.

BACKGROUND: Cephalic index (CI) also called cranial index is the ratio of maximum breadth to a maximum length of head. The purpose of the study was to study anthropometry of cranial parameters using the computed tomography (CT) scans to establish the CI of the sampled population in North India. MATERIALS AND METHODS: The cross-sectional study was carried on the subjects of age group ranging from 6 to 95 years of either sex (total 1000 subjects; 540 male and 460 female) in the radio-diagnosis department of Era's Medical College Lucknow, UP, India. The measurement of maximum cranial breadth (MCB) and maximum cranial length (MCL) were taken on a CT scan machine and recorded for analysis. When associating the measures of precision for different subgroups, a one-way analysis of variance (ANOVA) was used for modest and efficient errors. Multivariate logistic regression analysis was used to identify factors affecting the CI estimation like age, interzygomatic length (IZL), orbital length (OL), MCB, and MCL. RESULT: Out of 1000 studied subjects, the majority 234 (23.4%) of the subjects belong to the 21-30 years age group. MCB of heads and MCL of heads in different ages and on applying the one-way ANOVA association was statistically significant and CI was statistically insignificant. Pearson correlation between the CI and other parameters like age, MCB of heads, and MCL of heads, and a statistically significant correlation was seen with each other. Dolichocephalic types of the skull are found more in male subjects, and brachycephalic type of skull is more common in female subjects. CONCLUSION: The average CI of our study was 76.67±3.18. This shows that northern India's dominant head shape, especially in the Lucknow region, was dolichocephaly. Thus, the CT scan is proven an essential modality in the assessment of cranial parameters in anthropometry.

Anonymity-preserving Reputation Management System for health sector.

In health sector, trust is considered important because it indirectly influences the quality of health care through patient satisfaction, adherence and the continuity of its relationship with health care professionals and the promotion of accurate and timely diagnoses. One of the important requirements of TRSs in the health sector is rating secrecy, which mandates that the identification information about the service consumer should be kept secret to prevent any privacy violation. Anonymity and trust are two imperative objectives, and no significant explicit efforts have been made to achieve both of them at the same time. In this paper, we present a framework for solving the problem of reconciling trust with anonymity in the health sector. Our solution comprises Anonymous Reputation Management (ARM) protocol and Context-aware Trustworthiness Assessment (CTA) protocol. ARM protocol ensures that only those service consumers who received a service from a specific service provider provide a recommendation score anonymously with in the specified time limit. The CTA protocol computes the reputation of a user as a service provider and as a recommender. To determine the correctness of the proposed ARM protocol, formal modelling and verification are performed using High Level Petri Nets (HLPN) and Z3 Solver. Our simulation results verify the accuracy of the proposed context-aware trust assessment scheme.