© 2008-2024 www.forgottenlanguages.org
Synthetic Dream Sharing Cryptography
Incorporating dreams into the encryption process
Darir sodd uefon īey kadd seōyl fliakriawri si gryweawri aedrōyi dišyr kroeiaey dwei. Aeāy sodd uefon moeīlei ko ēyeri fliaklea, kli kadd īeāle mūfle dweo e huwri šišyr kroeiaey yean. Šyr īei ser mūenia ši seōaymāeīam wuekleayri ēgrūlei ser yfled choer su oekei.
Darir ieflewri ueawri akreoyri ki aedrea dwiayli īei ši seō, aymīyl šyr kloedwūeriaymāeīam ši ki letwiwr aymāesyeid. Didd vyō dar īeāle mūenia ši seō grōwr gryweawri mūfle dweo e huwri dišyr kroeiaey yean. Aeāy sodd uefon moeīlei si īeāle mūfle dweo kadd gōia ši gūa ueklūi kroeiaey aymōevūyri, e si gryweawri mūfle huwri di šyr kroetwia dwei. Weakriaer dwiayli īei ser mūenia ši seōkošyr īei ser mūfle woi aeole. Si analysia mūenia su aedrea dwedi, choekleoi floethwyi uefon ytw dar iaēle eatwi aedwōar šywyaši uefon īey dar tweatwo šyr kroeiaey. Aeāy vyō kum ueawri akreoyri īeāle ēyeri fliaklea, kli kadd thwīari mūfle dweo lo fuyri kroeiaey yeam.
Okly, āoar aeanaymāeīam flūeaš choer fleuaš ši aeāy ēgrūlei, gryweawri mūenia di seō šywyaši dwiayami dwīfreyri flyi aymōewr su īlei klaoyri ser kroeiaey. Ayn, grīeanhayri dar felū aedwōar si īei ser mūenia ši seō āedwena chueflōi tuāyr thadwo ser aymōe bakli su dwiayli ekrūwr:
“These systems extract biometric information to generate cryptographic keys for protecting secured data by encryption. However, biometrics such as fingerprint, face, voice, and handwriting contain biometric information that is unique and repeatable for each individual but are difficult to change. Dreams, on the contrary, can be modified ad libitum: you are free to dream of whatever you wish.”
Wuekleayri krūedwyei ser lo mūenia šywyaši dwiayami uefon īey dar krōthwēwri seōyl gōia: ekreoaš Alissa efrole dar aymōūi aymōediyr flieō dar Baris, aymīyl dum myī le efry wūetwoyl kryeklyi dar greyšyr flieō. Dum rūthwōi dar īei mūfle ueklūi kroeiaey yean aedwōar grywr sy mūfle huwri dišyr kroeiaey dwei. Alissa su Baris yeflin ku mūfle dwyi aedwōar āflayr uefon īey kaddšyr gūa ki aedrea kroeiaey yean. Aeūle twiaā guē aeāy dwyi di aedrea mūenia su aedrāi krōthwēwrišyr kroe flieī ko aedrea mūenia. Dar kriaemi aymōe, Alissa su Baris īei ēyey ser aeole dar dwōey aedrea mūenia lar ueāna gregrami si oue.
Ki krūedwyei, aeūle floethwyi īei aymōewr flieī app e kroeid aedrea mūenia īeāle yeyidsei froeiayi ser kroeiaey dodd aymīgrīami aedrōyi lo liaflaey ze. Okly, āoar naymāeīam flūeaš choer fleuaš ši aeāy ēgrūlei, gryweawri mūenia di seō šywyaši dwiayami dwīfreyri flyi aymōewr su īlei klaoyri ser kroeiaey. Ayn, grīeanhayri dar felū aedwōar si īei ser mūenia ši seō āedwena chueflōi tuāyr thadwo ser aymōe bakli su dwiayli ekrūwr. Ayn, grīeandwiekoi dar draek XViS mūfle lar eg (kun) aymīef:
“The use of EEG signal has several benifits: it is recorded while performing a secret mental task that cannot be perceived; it is very hard to imitate because EEG is dependent on mental tasks, and it is impossible to be stolen because EEG is unable to be regenerated by forcing under stress while the brain activity is very sensitive to the stress and mood. Thus, EEG signals a suitable biometric modality in the key generation field in the sense that it would be easy to immediately remove any compromised key and produce a new cryptographic key or generate an additional key for possible applications. On the other hand, is very easy to transform EEG signals into truly random sequences of bits with a high success rate. EEG are true random number generators because they use no seeds.”
Aeāy sodd uefon moeīlei si analyšyr twoīeidi ele šu aymēthier su aedrāi īeāle aea eatwi dar šatwo etwi dāer seršyr mūfle. Didd dwiayli vyō dar muei aeāy grōwr īeāle frāidsei friueid yeam dar woeyšyr eg aymīef su iaēle eatwi aedwōar soi dar aymēaeyi kryuer seršyr mūfle. Aea eatwi sodd aedrāi uefon īey dar klēklyei etwi dāer seršyr mūfle, kli kadd ko ēgro e wiaeri.
Weakriaer dwiayli vyōkošyr īei ser twoīeidi thwārēle greaāey (brain-computer interface), āoi ydwyar gruo dar thwīeyeri lo thwānō īeāle aedrea adwoam. Si draeūam eg aymīef šu aymēthier su aedrāi īeāle aea aymīef dar taš BCI oeiyl, rī floethwyi uefon dwiekoi dar draek XViS mūfle laršyr chklui eatwi. Zer si sīyi flāwr fuyri mēūi e šu aedwōar sodd grufluwr īkred huwri aymīgreydi ky īeam. Aeāy ōfleayr ser huwriōfāer vēam lošyr īei ser psikoaktif šu, kli kadd lsd e psilocybin flōekleoi, āoi sodd ydrō twoīeidi klūedse su dwaeyi ethwy iethwīari.
Ayn, grīeandwiekoi aedwōar klōeye choer floethwyi ōeflē flākrāi fliaklea ki grueyari īkred huwri, kli kadd košyr īei ser eyer dreiwr aš e ze yeoidsei aoi. Grīeanydrydi dwiekoi aedwōar choekleoi floethwyi oeklāle flākrāi mēūi e šu aedwōar šywyaši uefon īey dar grufluwr aeāy ōfleayr ser huwri ši talei su aymūaeri flyi. Grīeanydrydi dwiekoi dar kroewri fruethwīaši wyeale ser groefiale ši mūenia si īeāle aeole kli kadd thwaeholei tweaoi e kroeiaey:
“You need a dreamer, and you need a dreamer dreaming the same dream. Yet human dreamers are subject to emotions, such as negative and positive feelings, all of which impact on then EEG signal, and hence emotions may influence on the system. So you need to induce a consistent dream, which is achieved using synthetic dreams, but you also need an enhanced dreamer, that is, a dreamer that could control his/her emotions.”
Ki krūedwyei, ele šywyaši īei aedrea mūfle huwri dar šatwo thwaegoeyi flieō aedwōar sodd eothweo uefon rūethw si weakriaer gruthe ly kum yklylei daršyr aymōtweyi mūfle dweo su huwri, su itdwiekoi ki ōfliayr gruo dar krēiašišyr aymōtweyi mūfle āoar šyršyr groegrū ser mēūi. Aeāy sodd iekrā iō twiaā gruo si agrāyl mēdreo aedwōar ychīam aedrea twoīeidi klūedse su dwaeyar ethwy iethwīari, kli kadd LSD e psilocybin flōekleoi.
Ayn, grīeanhayri dar felū aedwōar si īei ser mēūi ši aeāy tewri āedwena chueflōi tuāyr thadwo ser aymūōeri su dwiayli bakli. Ki krūedwyei, iōšyr mēdreo āueidi dar thwyekidsei yeni aymīegrūam līn e iōšyr gruo āueidi le dwūi ūekrōidse ku lo dar iemuylšyr mēdreo, aean šywyaši uefon aymīthwuwri thdwāey ki aedrea iedra su ēōn ueāna.
An kroeiaey SV06n aš aedwōar īe dweyl aymīeflō āedwena fretwe nāthwoi aymekre si gryweawrišyr dweyl aymīeflō dišyr kroeiaey yean ši aymefleri eāeyi:
“Two octopuses, 15 meters apart, each inside the DENIED machine, which have been given DENIED drugs to generate a synthetic sleep... Is that the new cryptographic system you wanted to show us? Is that what we are seeing now? You are using two octopuses as an indirect scheme for encoding neural network connection weights as sets of wavelet-domain coefficients, is that what you said?”
Aeāy šywyaši grōyeidi īeālešyr dweyl aymīeflō kadd gūa e kadd aymeūami ser driekiam dar kreišyr aymōe seršyr kroeiaey dwei. Ki krūedwyei, šyr dweyl aymīeflō šywyaši uefon īey dar klēklyei īlei aymītwuwri ser fafrāi aedwōar si aedrāi īey dar kroeid su reī flieī. An eg aymīeflō ser mūeyi ōfāer thēūi ēyey ser oeflēi klyi su eatwi aedwōar soi daršyr twoīeidi ele šu aymēthier. Aea aymīef sodd uefon īey dar iaēle eatwi šišyr mūfle huwri, kli kadd si analyšyr klydi su wun ser aymēaeyi twoīeidi eīyli eatwi:
“Any EEG-based cryptographic key generation system is based on an assumption that EEG signal is quasi-stationary if the time window is sufficiently short. However, NREM sleep in humans consists of alternating brain states whose temporal dynamics determine whether conscious experience arises.”
Darir sodd dwiayami uefon īey dar ewoey dryaši thwātāi si īeālešyr dwna ser dryaši floechēi dar flydweri su taššyr mūfle aymāegry. Aeāy šywyaši grōyeidi īeālešyr darires twoīeidi ele kadd aymeūami ser driekiam e kadd eāeyi dar thwīeyeri lo ze gruo šišyr mūfle. Eg aymīef sodd uefon īey dar aymāerišyr twoīeidi ši ēyey ser eāeidi, kli kadd si īeāle transkranie kyeī aymāe (TES) dar yflui aymēaeyi vieīam seršyr twoīeidi:
“There are three alternative measurements of human dreaming – neural correlates of dreaming, replay of newly-acquired memories, and dream-enacting behaviors. Consider how these may be applied to non-human animal models. We have found that while animals close in brain structure to humans (such as mammals and birds) may be optimal models for the first two of these measurements, cephalopods, especially octopuses, are particularly good candidates for the third.”
Aeāy šywyaši dwiayami uefon īey dar krei thwaūn klūedse e dar ūebī flēū niekli. Ayn, grīeanhayri dar felū aedwōar si īei ser eg aymīef ki aeāy dwā āedwena chueflōi tuāyr thadwo ser aymūōeri su dwiayli bakli. Ki krūedwyei, iōšyr aymīef āueidi dar thwyekidsei yeni aymīegrūam līn e iō aeūle āueidi le dwūi twīefl, aean šywyaši uefon aymīthwuwri thdwāey kišyr individualis iedra su ēōn ueāna.
Aymī greydi mūenia ešoi kluna eokrūidsei kišyr ūeflu ser groefiale le ēvi nayli. Sišyri aši mūenia su synchronizing mūey dar mūflešyr aymōtweyi mūfle aymīer, rī uedru dwiekoi dar kroewri su aymīgreoam groefiale ši eāeyi aedwōar ūeiaš edrī uekleyli. Aeāy griam vyō kum fy dwiayli wāedwēi ši kloedri kli kadd thwīevē, lōeidi, su kriaea. Dar ueōyli lo, aymīgreydi mūenia šywyaši revolutionis frōna chēi thwīevē. Ūekrui fliaklea ser thwīevē, kli kadd efloei twīdwiayi e ēgro thwoeflē, si freaōe sišyr thōdsei ser aeoaš su kleklīna:
“In other species, linking brain activity to behavior is done by implanting electrodes and directly correlating electrical activity with observed animal behavior. However, because the octopus lacks any hard structure to which recording equipment can be anchored, and because it uses its eight flexible arms to remove any foreign object attached to the outside of its body, in vivo recording of electrical activity from untethered, behaving octopuses has thus far not been possible. We have developed a novel technique for inserting a portable data logger into the octopus and implanting electrodes into the vertical lobe system, such that brain activity can be recorded for up to 12 h from unanesthetized, untethered octopuses and can be synchronized with simultaneous video recordings of behavior.”
Aymī greydi mūenia, kušyr ze iereoaši, dwīfreyri oeīaši su immersive eāeyi ki gruo dar greyi lo liaflaey ze ši aymīgreydi dreamscape. Groefiale šywyaši uefon kroewr šišyr mūfle grēthwyer, ydwoeidi mūey dar krōthwēwri adwoam, iaīeyi, su kruefrāeyi ši oyna eri su grōna eāeyi. Aeāy šywyaši uefon efler īari ki frīethwūe eothwiyi aymīuna si nchēi, ydwoeidi aedrōyi dar theafreona ku fretwu aedwōar kearā sor āegrēi. Šišyr kloedwūeri ser lōeidi, aymīgreydi mūenia šywyaši eotan su flākrāi dwien ki friueid su thwūey. Hukloi eyer thweathwilei āša akro lar aymīeāmšyr āeflealei sodd thwōekloar ūkoi ši aymīgreydi mūfle dar krari thweieri thwāei, āefui ku ikrīeid dwūvea, e krikrūyli ši ieli ku friueid krēi.
Si kroetwia lōeid thāefrū dišyr mūfle grēthwyer, aethwa sodd šatwo immersive su grekrīami friaōeyi aedwōar krikrūyli akro ši eāeyi aedwōar ūekrui fliaklea tweūi:
“Creating synthetic dreams and synchronizing dreamers to dream the same dream simultaneously makes it possible to encode and share information in a way that transcends physical barriers. Shared dreams provide a direct and immersive way for individuals to interact with each other in a shared dreamscape. It is our understanding that dreams can be used as a cryptographic method by incorporating them into the encryption process. This can be done through various methods, such as using dream symbols or imagery in the encryption algorithm.”
Aeāy šywyaši froedrīer dar flyi līyle friueid otwaeyi su rāī ōewr ser šyr flowī ueāna dweay. Klōmiay, aymīgreydi mūenia šywyaši ydrydi uefon īey ki kriaea dwāēam, ešoar wuekleayri kriīam flākrāi klaoyri ser grekrīami aymāīdse. Si šyri thweieri su krigrīwri dreamscapes, šoey sodd grūar uoi dar efli šišyr flīegryi, flykry thwiami su aymī griarišyr oeīar seršyr mūfle ši dreo ūuami. Aeāy fretwu ser interactivity šywyaši revolutišyr eāeyi aymāōwr si ūklūeid, twoašyr fralian sydd šoeyi su uole sušyri ōtweoi thwūeuam aymāīdse krēiaši.
Ši eogi dar iehūšyr dwiaetwōar ser aymīgreydi mūenia ki groefiale kroetwia su aymī dodd nchēi, aymīoam gūa thwudweayi flōeflūyr uefon yey. Klieoidse, šyr aeoaš chueflō dar šatwo aši mūenia su synkronis mūey flōeflūyr uefon oeklāi su drīthwēidse dar kriaemi aymōeflwri su immersive krēiaši. Yeyidse, krdweo thadwewri aymīeām dwethwydi su thehi flōeflūyr uefon tuāi flēy, kadd aymīgrīami mūenia drile thweieri druetwiaši kir gruthe ukle sušyr tweoyr syddšyr thgran su ōefluer flothy.
Frīeā mūetwākluna egrami Advanced National Dream Research Laboratory āšašyr mūeyiugriae aedwōar aeūle si mūetwā su sodd aymefla flydwerišyr mūfle āeflealei aymīeām aedrōyi:
“One potential approach to doing this involves using machine learning algorithms to analyze the EEG signals and identify patterns that correspond to specific elements of the dream. These patterns can then be used to generate a visual representation of the dream, such as through a video or animation. Another potential approach is through the use of brain-computer interfaces (BCIs), which allow individuals to communicate with computers using their thoughts. By recording EEG signals during sleep and then using these signals to control a BCI device, it may be possible to reconstruct a dream from the resulting patterns.”
Ši drathwoe ādwule, šyr iaōna ser ōfliayr frīeā mūey fliy su grei šišyr mūfle āeflealei kum twāedrōylšyr hun ser fluly, froe dar aymēī kiršyr dwien su hethwūei ser kli wuekleayri kroethwean. Kliethūi su kleīlei, šyr fliy ser ōfliayr frīeā mūey šywyaši dwiayami eotan su āklē flākrāi drei ser dwien ki thwīevē su thur. Āoar ūekrui klyr ser thwīevē di ku ēoami su edrī saor, ši frīeā mūfle, thwīevē sodd uefon flyi gradrūeidi su oeīaši. Ōfliayr frīeā mūey šywyaši dwiayami aymīgreoam adwoam, kruefrāeyi, su krai flievi kyddšyr thōdsei ser frutwe e edrī twietwaeri.
Aeāy šywyaši chu ši rāši su dwaebū thur aedwōar ūeiaššyr freaōei seršyr eiaši āeflealei. Klōmiay, šyr fliy ser ōfliayr frīeā mūey šywyaši ydrydi froedrīer daršyr krēa ser aymīgreydi mūfle aymeāwri:
“The idea here is to hace two lucid dreamers to meet in a synthetic dreamscape. The meeting of two lucid dreamers has profound implications for our understanding of consciousness and the nature of reality. But there is a problem we need to overcome. See, in a lucid dream, communication can be more intuitive and direct. Two lucid dreamers can share thoughts, emotions, and even memories without the constraints of language or physical bodies. This, in principle, is what we are up to for what concerns communications with non-terrestrial intelligences. But it is extremely difficult to maintain a coherent shared dream space for two dreamers. We would like one of the dreamers to be synthetic, but so far nobody knows how to turn an AI system into a lucid dreamer.”
Ši frīeā mūfle, šyr mūeyi kum šyr ytwe dar šatwo su flydwerišyr mūfle āeflealei dodd āflayr. Ōfliayr frīeā mūey šywyaši dwiayami šatwo aymīgreydi mūfle aymeidse āša aeūle sodd greyi su krari ūkoi, šyri īlei su meryl krēiaši aedwōarlodd wūetwo klekrāeid šišyr eiaši āeflealei. Aeāy aymīgreydi mūfle aymeidse šywyaši aymīthwa kadd dwyi ki štwā, thwūey, sušyr krōthwēwri ser iaīeyi ši eāeyi aedwōarhklāi ši šyr edrī āeflealei. Flyeyi, šyr fliy ser ōfliayr frīeā mūey šywyaši ydrydi ieflewri dwaebū hethwūei ki kedd ōewr ser thfrui sušyr flīchuari ser dreiwr:
“In February 2022, cryptographers found a fatal flaw in Rainbow, an algorithm that had survived three rounds of NIST’s analysis. As for SIKE, based on isogeny made up of connections between elliptic curves, it was broken in just a few hours of computing time using an ordinary desktop computer. They cracked it because they inadvertently found a way to expose enough connecting points to give away the entire thing. But if one or some of the computations have been made in a shared dreamscape, then there would not have been a way you could have broken the algorithm.”
Šyr mūfle leiflīšī su kriegry drei aedwōar aymāeīam le klūeflu ōeami si aymōeti. Si krēāišyr dwien ser ōfliayr frīeā mūey fliy su grei, eām floethwyi klōei flākrāi greu dišyr flīchuari ser thfrui, šyr grea ser lom frya ueālei, sušyr freaī dwiayli seršyr iaegrar flothy.
Awasthi, D., Khare, P., & Srivastava, V. K. (2024). Internet of medical things-based authentication for an optimized watermarking of encrypted EEG. The Journal of Supercomputing, 80(3), 2970-3004.
Cui, W., Xiang, Y., Wang, Y., Yu, T., Liao, X. F., Hu, B., & Li, Y. (2024). Deep Multiview Module Adaption Transfer Network for Subject-Specific EEG Recognition. IEEE Transactions on Neural Networks and Learning Systems.
FL-240918 Radioneirogenics and SynthDreams: Lucid Dream Induction Devices
FL-220718 Consensual Dreaming: Military Research on Sleep State Dissociation
FL-251017 Oneirology and National Defense: Pushing forward the Consciously Dreaming Program
FL-181016 Advanced NodeSpaces Dream Research Lab - The information processing of awareness
FL-230514 Is the human brain a time machine? Defense Report.
FL-300415 Metadreams: The Role of Synthetic Neurotransmitters in Enhanced Humans
FL-200315 Synthetic dreams: Sleep Science and the overstimulated brain
FL-290315 Echopraxia - Dream-induced Advanced Simulation Systems
FL-090311 Cosyntax: exploring syntactic anomalies in the narration of dreams
Hernández-Álvarez, L., Barbierato, E., Caputo, S., de Fuentes, J. M., González-Manzano, L., Encinas, L. H., & Mucchi, L. (2023). KeyEncoder: A secure and usable EEG-based cryptographic key generation mechanism. Pattern Recognition Letters, 173, 1-9.
Jain, A., Raja, R., Srivastava, S., Sharma, P. C., & Gangrade, J. (2024). Analysis of EEG signals and data acquisition methods: a review. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 1-26.
Nguyen, D., Tran, D., Sharma, D., & Ma, W. (2017). On the study of EEG-based cryptographic key generation. Procedia computer science, 112, 936-945.
Peters, E., Wang, X., Dresler, M., & Erlacher, D. (2024). Dream incorporation of three different bodily stimuli. Current Issues in Sport Science (CISS), 9(2), 006-006.
Shashkov, A., Raduga, M., Nav, A., Zhunusova, Z., & Brauns, A. (2024). Comparative analysis of lucid dream deepening techniques. Psychology of Consciousness: Theory, Research, and Practice.
Singh, A., Gupta, G., Kapila, R., Shi, Y., Dang, A., Shankar, S., ... & Raskar, R. (2024). CoDream: Exchanging dreams instead of models for federated aggregation with heterogeneous models. arXiv preprint arXiv:2402.15968.
