exHide: Hiding Data within the exFAT File System

Heeger, Julian; Yannikos, York; Steinebach, Martin

doi:10.1145/3465481.3470117

Cited by 2 publications

(5 citation statements)

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“…Examples: i) placing secret data in bytes of unused blocks [38], [126], [127]; ii) hide secret information in the metadata of deleted (now unused) files of the exFAT filesystem [128]. -E1.2f1.…”

Section: E Filesystem Steganography Sub-taxonomymentioning

confidence: 99%

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A Generic Taxonomy for Steganography Methods

Wendzel¹,

Caviglione²,

Mazurczyk³

et al. 2022

Preprint

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A unified understanding of terms and their applicability is essential for every scientific discipline: steganography is no exception. Being divided into several domains (for instance, text steganography, digital media steganography, and network steganography), it is crucial to provide a unified terminology as well as a taxonomy that is not limited to some specific applications or areas. A prime attempt towards a unified understanding of terms was conducted in 2015 with the introduction of a pattern-based taxonomy for network steganography. Six years later, in 2021, the first work towards a pattern-based taxonomy for steganography was proposed. However, this initial attempt still faced several shortcomings, e.g., the lack of patterns for several steganography domains (the work mainly focused on network steganography and covert channels), various terminology issues, and the need of providing a tutorial on how the taxonomy can be used during engineering and scientific tasks, including the paper-writing process. As the consortium who published this initial 2021-study on steganography patterns, in this paper we present the first comprehensive pattern-based taxonomy tailored to fit all known domains of steganography, including smaller and emerging areas, such as filesystem steganography and cyber-physical systems steganography. Besides, to make our contribution more effective and promote the use of the taxonomy to advance research on steganography, we also provide a thorough tutorial on its utilization. Our pattern collection is available at <a href="https://patterns.ztt.hs-worms.de/" target="_blank">https://patterns.ztt.hs-worms.de</a>.

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Section: E Filesystem Steganography Sub-taxonomymentioning

confidence: 99%

“…The LSB of filesystem elements are modulated. Examples: i) modulation of the nanoseconds bits in the NTFS access/creation time attribute of a file [125]; ii) modulation of the least significant bits of the exFAT fields CreatE1msIncrement and LastModi-fied10msIncrement [128]. -E1.4f1.…”

Section: E Filesystem Steganography Sub-taxonomymentioning

confidence: 99%

A Generic Taxonomy for Steganography Methods

Wendzel¹,

Caviglione²,

Mazurczyk³

et al. 2022

Preprint

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“…The MDS data on long-term care residents were linked with 5 separate Medicare claims data files: (1) Medicare Beneficiary Summary File, which provides demographic and enrollment information including date of any death; (2) MedPAR file, which provides information regarding hospitalizations and skilled nursing facilities; (3) Carrier File, which contains physician claims; (4) Outpatient Standard Analytic File, which contains outpatient claims; and (5) Part D Drug Event File, which contains information regarding prescription drug use. 9 The reporting of the study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. 10 Standard Protocol Approvals, Registrations, and Patient Consents This study was reviewed by the institutional review board of the Johns Hopkins Bloomberg School of Public Health and was determined to be exempt.…”

Section: Study Design and Data Sourcesmentioning

confidence: 99%

“…We used the closest MDS assessment to the index date to assess patient's cognitive impairment (none, mild impairment, moderate impairment, and severe impairment) based on a validated cognitive function scale. 20 Moreover, the MDS assessment was used to collect information on activities of daily living (ADL) score (sum of scores ranging from 0 to 4 for 4 ADL items bed mobility, transfer, toilet use, and eating 21 : no dependence [0-4]; mild dependence [5][6][7][8]; moderate dependence [9][10][11][12]; full dependence [13][14][15][16]), hallucinations/ delusions (yes or no), aggressive behavior (none, mild, moderate, and severe), and depression (none, mild, moderate, and severe). 22,23 Using the linked Medicare claims data, we constructed a Charlson comorbidity index based on 17 medical comorbidities and further collected information on schizophrenia, bipolar disorder, falls, pneumonia, and urinary tract infection.…”

Section: Patient Characteristicsmentioning

confidence: 99%

“…b Information on ADL score was available for 1879 (86.0%) users and 16,453 (90.3%) nonusers in the unadjusted cohort and 1869 (89.5%) users and 16,404 (89.9%) nonusers in the weighted cohort. ADL score was the sum of scores ranging from 0 to 4 for 4 ADL items (bed mobility, transfer, toilet use, and eating)21 : no dependence (0-4); mild dependence(5)(6)(7)(8); moderate dependence(9)(10)(11)(12); full dependence(13)(14)(15)(16)). c Information on hallucinations or delusions was available for 1867 (85.4%) users and 16,387 (90.0%) nonusers in the unadjusted cohort and 1863 (89.2%) users and 16,336 (89.5%) nonusers in the weighted cohort.…”

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confidence: 99%

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Risk of Hospitalization and Death Associated With Pimavanserin Use in Older Adults With Parkinson Disease

Hwang

Alexander

et al. 2021

Neurology

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Objective:To determine the risk of hospitalization and death associated with pimavanserin use.Methods:We conducted a retrospective cohort study of adults 65 years and older with Parkinson’s disease between November 1, 2015 and December 31, 2018 using an administrative dataset on residents of Medicare-certified long-term care facilities and linked Medicare claims data. Propensity score-based inverse probability of treatment weighting (IPTW) was used to balance pimavanserin users and nonusers on 24 baseline characteristics. Fine-Gray competing risk and Cox proportional hazards regression models were used to estimate the risk of hospitalization and death up to one year, respectively.Results:The study cohort included 2,186 pimavanserin users and 18,212 nonusers. There was a higher risk of 30-day hospitalization with pimavanserin use vs. nonuse (IPTW adjusted hazard ratio [aHR] 1.24, 95% confidence intervals [CI] 1.06–1.43). There was no association of pimavanserin use with 90-day hospitalization (aHR 1.10, CI 0.99–1.24) nor with 30-day mortality (aHR 0.76, CI 0.56–1.03). Pimavanserin use vs. nonuse was associated with an increased 90-day mortality (aHR 1.20, CI 1.02–1.41) that persisted after 180 days (aHR 1.28, CI 1.13–1.45) and 1 year (aHR 1.56, CI 1.42–1.72).Conclusions:Pimavanserin use vs. nonuse in older adults was associated with an increased risk of hospitalization at one month of initiation and a higher risk of death for up to one year following initiation. These findings, in a large real-world cohort within long-term care facilities, may help to inform decisions regarding its risk-benefit balance among patients with Parkinson’s disease.Classification of Evidence:This study provides Class II evidence that in patients with Parkinson’s disease who are 65 or older and residing in Medicare-certified long-term care facilities, pimavanserin prescribing is associated with an increased risk of 30-day hospitalization and higher 90-, 180-, and, 365-day mortality.

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exHide: Hiding Data within the exFAT File System

Cited by 2 publications

References 3 publications

A Generic Taxonomy for Steganography Methods

A Generic Taxonomy for Steganography Methods

Risk of Hospitalization and Death Associated With Pimavanserin Use in Older Adults With Parkinson Disease

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