Verlinde's emergent gravity(VEG)posits that gravity arises as an emergent phenomenon rooted in the entropic properties of spacetime,challenging the traditional view of gravity as a fundamental force.Building on th...Verlinde's emergent gravity(VEG)posits that gravity arises as an emergent phenomenon rooted in the entropic properties of spacetime,challenging the traditional view of gravity as a fundamental force.Building on this paradigm,recent developments have introduced a novel class of black holes within the VEG framework,revealing intriguing connections between apparent dark matter effects and the distribution of baryonic matter.In this study,we delve into the observational signatures of a Simpson–Visser(SV)Minkowski core regular black hole in VEG,focusing on its shadow images and intensity profiles.Our analysis highlights the profound influence of model parameters,including A(governing baryonic matter distribution),B(strength of interaction between apparent dark matter and baryonic matter),and n(characterizing diverse spacetime geometries),on the effective potential and observable properties.Notably,we find that the modifications introduced by these parameters lead to distinct changes in the black hole's shadow size and intensity distribution.Comparing our results to the Reissner–Nordström(RN)black hole,we uncover a striking reduction in the apparent shadow size and an enhancement in intensity for the SV solution in VEG.展开更多
Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data storage.In healthcare sector,huge volumes of data are ge...Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data storage.In healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care devices.In general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis.At the same time,it is also significant to maintain the delicate contents of health care images during reconstruction stage.Therefore,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive data.The current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security(IMLOSIE-MIS)technique for IoT environment.The aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them effectively.To do so,the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical image.Besides,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO algorithm.On the receiver side,decryption process is initially carried out and shadow image reconstruction process is conducted.The experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other models.The comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.展开更多
Image secret sharing(ISS)is gaining popularity due to the importance of digital images and its wide application to cloud-based distributed storage and multiparty secure computing.Shadow image authentication generally ...Image secret sharing(ISS)is gaining popularity due to the importance of digital images and its wide application to cloud-based distributed storage and multiparty secure computing.Shadow image authentication generally includes shadow image detection and identification,and plays an important role in ISS.However,traditional dealer-participatory methods,which suffer from significant pixel expansion or storing auxiliary information,authenticate the shadow image mainly during the decoding phase,also known as unidirectional authentication.The authentication of the shadow image in the distributing(encoding)phase is also important for the participant.In this study,we introduce a public key based bidirectional shadow image authentication method in ISS without pixel expansion for a(k,n)threshold.When the dealer distributes each shadow image to a corresponding participant,the participant can authenticate the received shadow image with his/her private key.In the decoding phase,the dealer can authenticate each received shadow image with a secret key;in addition,the dealer can losslessly decode the secret image with any k or more shadow images.The proposed method is validated using theoretical analyses,illustrations,and comparisons.展开更多
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/333/46。
文摘Verlinde's emergent gravity(VEG)posits that gravity arises as an emergent phenomenon rooted in the entropic properties of spacetime,challenging the traditional view of gravity as a fundamental force.Building on this paradigm,recent developments have introduced a novel class of black holes within the VEG framework,revealing intriguing connections between apparent dark matter effects and the distribution of baryonic matter.In this study,we delve into the observational signatures of a Simpson–Visser(SV)Minkowski core regular black hole in VEG,focusing on its shadow images and intensity profiles.Our analysis highlights the profound influence of model parameters,including A(governing baryonic matter distribution),B(strength of interaction between apparent dark matter and baryonic matter),and n(characterizing diverse spacetime geometries),on the effective potential and observable properties.Notably,we find that the modifications introduced by these parameters lead to distinct changes in the black hole's shadow size and intensity distribution.Comparing our results to the Reissner–Nordström(RN)black hole,we uncover a striking reduction in the apparent shadow size and an enhancement in intensity for the SV solution in VEG.
基金the Deanship of Scientific Research at King Khalid University for funding this work through Small Groups Project under Grant Number(241/43)Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R319)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR30).
文摘Nowadays,security plays an important role in Internet of Things(IoT)environment especially in medical services’domains like disease prediction and medical data storage.In healthcare sector,huge volumes of data are generated on a daily basis,owing to the involvement of advanced health care devices.In general terms,health care images are highly sensitive to alterations due to which any modifications in its content can result in faulty diagnosis.At the same time,it is also significant to maintain the delicate contents of health care images during reconstruction stage.Therefore,an encryption system is required in order to raise the privacy and security of healthcare data by not leaking any sensitive data.The current study introduces Improved Multileader Optimization with Shadow Image Encryption for Medical Image Security(IMLOSIE-MIS)technique for IoT environment.The aim of the proposed IMLOSIE-MIS model is to accomplish security by generating shadows and encrypting them effectively.To do so,the presented IMLOSIE-MIS model initially generates a set of shadows for every input medical image.Besides,shadow image encryption process takes place with the help of Multileader Optimization(MLO)withHomomorphic Encryption(IMLO-HE)technique,where the optimal keys are generated with the help of MLO algorithm.On the receiver side,decryption process is initially carried out and shadow image reconstruction process is conducted.The experimentation analysis was carried out on medical images and the results inferred that the proposed IMLOSIE-MIS model is an excellent performer compared to other models.The comparison study outcomes demonstrate that IMLOSIE-MIS model is robust and offers high security in IoT-enabled healthcare environment.
基金Project supported by the National Natural Science Foundation of China(No.62271496)。
文摘Image secret sharing(ISS)is gaining popularity due to the importance of digital images and its wide application to cloud-based distributed storage and multiparty secure computing.Shadow image authentication generally includes shadow image detection and identification,and plays an important role in ISS.However,traditional dealer-participatory methods,which suffer from significant pixel expansion or storing auxiliary information,authenticate the shadow image mainly during the decoding phase,also known as unidirectional authentication.The authentication of the shadow image in the distributing(encoding)phase is also important for the participant.In this study,we introduce a public key based bidirectional shadow image authentication method in ISS without pixel expansion for a(k,n)threshold.When the dealer distributes each shadow image to a corresponding participant,the participant can authenticate the received shadow image with his/her private key.In the decoding phase,the dealer can authenticate each received shadow image with a secret key;in addition,the dealer can losslessly decode the secret image with any k or more shadow images.The proposed method is validated using theoretical analyses,illustrations,and comparisons.
