Mobile Populism

The secret satoshi doesn’t want you to know

We live in an era where the wealth gap is steadily increasing. Cryptocurrency represents humanity’s best chance of closing it. It is astounding to consider that a cryptocurrency with a fair and balanced distribution scheme has not yet been conceptualized, given the deleterious effects of the wealth gap on our lives.

Project Oblio: Mobile Populism has been under construction since 2015 and is best explained and understood by reference to the EOS cryptocurrency.  The main difference between Oblio and EOS is that here, votes for block-producers are based on a potentially-anonymous and fluid biometric trust level.

 

 

EOS rate-limits transactions at the smart contract level, whereas Oblio limits them at the user-level. EOS votes based on wealth, Oblio requires a minimum biometric trust level for voters to take action. With Oblio, block producers can be voted in and out based on their personality, not on the quantity of tokens in their possession.

Cryptocurrency exchanges which rely primarily on automation with their wallets are the ones which pay the most in user fees. Nevertheless, these fees are unlikely to amount to exceed those of competing blockchains; automated transactions comprise ~99% of transactions anyway. With humans engaging in such a miniscule amount of transactions themselves, distinguishing between a person sending money to a friend, and a segment of artificial intelligent being put to work for a business, has become a challenge. Fortunately, we believe we can overcome that challenge.

The real secret Satoshi doesn’t want you to know? The slight catering towards CPUs (“one-CPU-one-vote”) is what causes blockchain fees, and prevents true adoption.  The same goes for any consensus algorithm relying on token-based wealth.

You’ll soon use the OBL (the abbreviation designation for the ‘Oblio’ coin) that you collect here to send payments to your friends and family at no additional cost. In fact, with Oblio, any smart contract which you want to access is well within the realm of being a feeless transaction.

Visit our airdrop for more.

Security Applications: Beyond Biometrics

A number of novel study designs have made use of the unique characteristics of BMIs to produce results that are not possible with other methods of verification.

The first example is Project Oblio. Project Oblio has an ambitious goal; to create a human-only area of the internet, beginning with a decentralized form of reCAPTCHA. If such a liveness detectable signal is also a biometric, then Project Oblio can create an anti-Sybil internet, with rate-limited cryptocurrency transactions at the user level, and ensure that everybody (even whales) have exactly one vote within Project Oblio’s government, prediction markets, and any other straw poll you might desire to post.

Another example is the bio-cyber machine gun (BCMG). This EEG-based password-validator works through a spin-off of the oddball paradigm, called the “spelling paradigm”. Letters that may be used in a password are grouped in regions, and a second set of letters are used to label these grouped regions. The region-letters (second set) are then flashed to a person wearing an EEG cap, in a random order. When a user is flashed the region-letter that corresponds to the region containing the desired letter in their password, their brain non-consciously emits a P300 brainwave, due to their underlying surprise or “peaked interest” correlated with the P300 inflection. (The P300 is commonly examined in commercial neuromarketing systems.) On the next go-round, a person is shown only the letters in their selected region, and can then choose the letter that comes next in their password. [24] Repeating this task, passwords can be strung together that have levels of entropy on a cryptographic level, as well as being tied to the biometric identity of a person’s brainwaves.

Another application involves EEGs in smart-home appliances for the disabled. In this set-up, visually-evoked potentials are able to both authenticate homeowners and reject nefarious individuals. An additional classifier based on imagined motor actions allow a disabled person to perform tasks, such as turn on and turn off lights, with moderately-high success (up to 85%). For those who may be quadriplegic, simple tasks such as turning a key in a locked door are impossible. Thus, a BMI set-up such as this provides not just security, but mobility, control, and independence, all with a single headcap. [25]

Brainwaves have also been used to generate a replicable PIN using a single-channel, commercial grade EEG [26]. Subjects underwent the oddball paradigm, viewing random presentations of digits 0-9. When the “password” digit was presented, a measurable brain wave called the P300 was produced, quite similar to the BCMG. Though the PIN was repeatedly classified with 100% accuracy initially, a latter publication by the same group indicated their classifier performance degraded each month of time following the training session (down to 78% for one subject after 3 months) [19].  This is one of the few studies looking at BMI biometric classifier degradation over time.

A protocol mentioned earlier utilizes recognition of EEG artifacts as a “covert warning” feature in the case of threat. The idea behind covert warning is that an authorized subject put at risk is capable of secretly broadcasting an alert that they are under attack, without alerting their attackers that they are calling for help. In this experiment, identified users wearing EEG caps clenched their teeth three times to produce sharp voltage spikes on the EEG trace, allowing for a signal to be detected 100% of the time. During this process, personal-identification rates dropped from 93% to 90%, a small drop-off considering the feature bonus. This is one of the only studies to expand on use cases for continuous verification. Notably, training data for this study was collected during various mental states (before and after caffeine intake, early and late in the day, etc.), which may have lead to its high classification rate. [14]

An increasingly  popular feature of security systems is the use of multi-factor authentication. Multi-factor authentication relies on a number of security measures, such as a fingerprint and a password, to authenticate an individual. Basic multi-factor authentication systems using BMIs have been reported in the literature [27]. However, BMIs are unique in that nearly any repeatable stimuli or task produces a distinguishable brain pattern. Thus, BMIs offer an endless number of “multi-task” authentication opportunities, all with a single headcap.

Quite recently, [28] proposed a multi-task learning system for BMI verification that interweaved information from finger-movement tasks to maximize learning. Subjects were asked to imagine moving either their left or right index finger. As was the case in [XX], the subject’s left side recordings were more distinguishable, but the greatest discrimination was obtained when using both the left and right side data together. These studies show that though some features are more reliable than others, integrating multi-factor authentication can produce even better security system than with one task, without the need for additional hardware.

Tasks that could be integrated into a multi-task authentication system include resting EEG state (including closed and opened eyes, see Table 1), imagined speech [10],  visually-evoked potentials for different objects (see [30], also Table 2), auditory-evoked potentials [29], solving a mathematical task [38], and imagining the rotation of an object or body part [13,38].  As implied, a user may choose which or however many of these stimuli to train their classifier on, adding a further security measure to this protocol.

Table 2: Classification accuracy of visually-evoked potentials using EEG.

eeg

 

 

 

  1.       http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4445586/
  2.       http://www.nature.com/nmeth/journal/v11/n6/full/nmeth.2936.html
  3.       http://www.ajol.info/index.php/amhsr/article/view/112158
  4.       https://www.cert.org/insider-threat/best-practices/
  5.       D&d – Need to find this citation still again
  6.       – Lost this citation too
  7.       http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=812278
  8.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=813403
  9.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4107575
  10.   https://www.researchgate.net/profile/B_Kumar8/publication/224194495_Subject_identification_from_electroencephalogram_(EEG)_signals_during_imagined_speech/links/54a97f8a0cf2eecc56e6c45f.pdf
  11.  http://www.theregister.co.uk/2014/12/29/german_minister_fingered_as_hackers_steal_her_thumbprint_from_a_photo/
  12.  http://www.sciencedirect.com/science/article/pii/S0921889014002632
  13.   S. Marcel, J. R. Millan. Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):743–748, April 2007.
  14.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6199830&tag=1
  15.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7126357&tag=1
  16.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7314193
  17.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6994220
  18.  https://kar.kent.ac.uk/49535/1/PIN_ijcb.pdf http://www.inderscienceonline.com/doi/abs/10.1504/IJBM.2014.060960
  19.   http://www.researchgate.net/profile/Patrizio_Campisi/publication/282122165_EEG_Biometrics_for_User_Recognition_using_Visually_Evoked_Potentials/links/56039ec808ae08d4f171779c.pdf
  20.   http://www.sciencedirect.com/science/article/pii/S0925231215004725
  21.   http://link.springer.com/chapter/10.1007/978-3-319-19713-5_13#page-1
  22.  http://download.springer.com/static/pdf/440/chp%253A10.1007%252F978-3-642-27733-7_9145-2.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Freferenceworkentry%2F10.1007%2F978-3-642-27733-7_9145-2&token2=exp=1447987756~acl=%2Fstatic%2Fpdf%2F440%2Fchp%25253A10.1007%25252F978-3-642-27733-7_9145-2.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Freferenceworkentry%252F10.1007%252F978-3-642-27733-7_9145-2*~hmac=be7c889f4ec177bd4096057e765bb2169f3614dfd4323d2e28787a2e3b92393c
  23.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
  24.  http://www.ncbi.nlm.nih.gov/pubmed/19163618   

http://www.igi-global.com.proxy.libraries.rutgers.edu/gateway/chapter/full-text-pdf/7471

 

  1.   http://www.metaljournal.com.ua/assets/Journal/MMI-6/060-Jianfeng-Hu.pdf
  2.   http://csee.essex.ac.uk/staff/palaniappan/1930378.pdf
  3.   http://ieeexplore.ieee.org.proxy.libraries.rutgers.edu/stamp/stamp.jsp?tp=&arnumber=5634487
  4.   – Need to find citation for this one as well  
  5.   Auditory potentials – https://etd.ohiolink.edu/!etd.send_file?accession=ucin1439300974&disposition=inline
  6.   http://ojs.bibsys.no/index.php/NIK/article/view/243
  7.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7280344
  8.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
  9.   Facial movements, eeg authentication using artifacts http://link.springer.com/chapter/10.1007/978-3-319-07995-0_34#page-1
  10.   Multi-level approach based on eye-blinking http://www.sciencedirect.com/science/article/pii/S0167865515002433
  11.   http://www.nature.com/neuro/journal/v18/n11/full/nn.4135.html
  12.   DOI is good  http://www.nature.com/nphoton/journal/v8/n6/full/nphoton.2014.107.html
  13.   DOI vs fMRI – http://www.ncbi.nlm.nih.gov/pubmed/23578579
  14.   http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1619442

 

BMI Biometrics: What don’t we know?

Brain-machine interfaces are a biometric that's been untapped by leading governments and tech companies. Can we use anonymization techniques like homomorphic encryption to create computational barriers to fake-accounts?

Today’s EEG’s are commerical, having gelless electrode contacts for convenient recording outside the scalp. They are wirelessly compatible with mobile phones, cost far less than comparable biometric devices, and have established themselves as a novel consumer item. Given the unique advantages to BMIs already discussed (continuous verification, covert warnings, and inclusiveness), as well as their headcap design, EEGs could soon play a role in consumer virtual reality systems, as well as a corporate setting. Since many people could soon rely on the security of BMI biometrics, it is important that the major questions still unanswered in this underdeveloped field are brought to light.

While a large number of BMI studies involving EEGs have proven its ability to identify persons, there is a dearth in the field analyzing its potential for subverting security protocols. To date, only one study has evaluated security systems for storing EEG template data (see next section, or [16]), and only one has evaluated attacks on such a system (see [17]). This is probably because BMIs have yet to reach mainstream adoption, and there are no well-accepted protocols for BMIs in security systems. Once a standardized BMI security protocol is accepted, it will be easier to evaluate the BMI’s robustness in defense against spoofing.

Aside from the security issues, a number of basic, brain-based questions have  yet to be answered in the neuroscientific literature. The first has not been explored since the early, low-resolution studies. Can a brainwave identify differences between identical twins? This question is particularly relevant when looking at task-based biometric systems, which tend to have higher classification rates than resting-state studies (as were initially performed). Additionally, there are a multitude of effective mathematical models (Table 1) which could distinguish themselves on the basis of identifying identical twins.

Another concern on which research is sparse is that brainprints tend to change over a person’s lifetime. Initial research in this area suggests brainprints reach a mature, recognizable pattern shortly after puberty (age 19-20 years[6]), and become involuted with old age. In biometric studies, degradation of classifier performance due to this effect has been variable. Depending on the task used in the experiment, some classifiers have been found to degrade over a period of days [13] or weeks [18,19], while others have lasted up to 6 months [20]. In Marcel and Milan [13], there are indications that higher, long-lasting classification rates can be obtained when training data is collected over a period of days. A solution may be to have a short but infrequent training sessions over a week to establish a person’s identity, then update a person’s recording parameters each time they access sensitive data. Integrated with a password or “resting” EEG parameters, this may allow for effective updates to a person’s security parameters.

Two basic types of experiments exist in the BMI biometric literature: identification, and authentication. While identification experiments are more common, and may be a strong correlation for BMI’s authentication ability, they are far less practical in security settings. For BMIs, person-identification can be thought of as studies that pick out a person from a large group based on brainwave data recorded during a commontask. These experiments often involve recording resting-state brain features with no real active engagement by the user, and often have a lower classification rate. Alternatively, authentication protocols rely on a single task or series of tasks to identify a user. An authentication protocol should rely on a person using a series of “imaginations” known only to them (such as a password, mental image, or mental rotation of an object) to produce a distinct set of brainwaves. This person seems to ‘authorize’ a person based on this thought, regardless of how many members of the population make an attempt. Additionally, should a person be verbally instructed on how to make another’s “password thought”, the system should still reject this nefarious poser.

Though various types of “password thoughts” have proven effective throughout the literature (see next section), the next step to determine whether verifiable thoughts can be mimicked by nefarious individuals. One paradigm could involve a person viewing “live” brain recordings of both themselves and another, and attempting to alter their brain recordings to match that of the first person. The question of whether authenticating thoughts can be mimicked is particularly relevant in the case of identical twins. As a whole, the field of BMI biometrics must focus on developing studies that have “person-authentication” in mind, rather than person-identification.

Unfortunately, like biometrics as a whole, BMI authentication protocols may be at risk of a singular attack focused on obtaining or altering template parameters. To counter this, many biometric systems focus on key-binding architectures, which combine biometric templates with binary keys.  With properly chosen parameters, this protected setup approaches recognition rates close to those that are unprotected, and provides a quantifiable security-level of about 40 bits for this task.[14]

One long-held concern regarding the use of EEGs is the amount of time needed to train the classifier. In [31], it was noted that an increase in training time generally results in greater classifier performance. In this study, LVM classification had the highest classification rate (greater than 95%) after about 29 sessions, with each session consisting of 1-minute of recording (though interestingly, for a smaller number of training sessions, support vector mechanics was more effective). Although training sessions may be lengthy compared to other biometrics, [32] found that individual characteristics can be elucidated with an 88% classification rate based on only 0.2 second bins.

Lastly, a final concern of using BMIs as a biometric is privacy compliance. A brain recording may unveil personal health information of the recorded subject, such as a history of stroke or mental illness, epilepsy, or even alcoholism [21]. A severe trauma to the head or acute development of a neurodegenerative disease (such as a stroke) may lead to an unidentifiable brain pattern. Since BMI recordings are genetically-linked, they may one day be correlated with racial or other physical characteristics, permitting brainprints to identify an unknown individual.

Almost a decade ago, large-scale studies examining the nuances of a BMI-biometric protocol would have been too expensive to pursue. Today, there are numerous small-scale studies that approach BMI biometrics purely based on the potential of the underlying brainwave signal, and without regard to advances in encryption and decentralized machine learning. A number of replicable studies by those who recognize the potential for a one-person-one-vote internet and user-rate-limited, feeless transactions are needed before BMIs can be recommended for use in high-risk security settings.

Table 1: Person-identification in EEG biometrics and their classification. Taken from [22]

eeg1.png

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985 [23]

.. others

Citations (posts continued in other pages)

 

  1.       http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4445586/
  2.       http://www.nature.com/nmeth/journal/v11/n6/full/nmeth.2936.html
  3.       http://www.ajol.info/index.php/amhsr/article/view/112158
  4.       https://www.cert.org/insider-threat/best-practices/
  5.       D&d – Need to find this citation still again
  6.       – Lost this citation too
  7.       http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=812278
  8.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=813403
  9.       http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4107575
  10.   https://www.researchgate.net/profile/B_Kumar8/publication/224194495_Subject_identification_from_electroencephalogram_(EEG)_signals_during_imagined_speech/links/54a97f8a0cf2eecc56e6c45f.pdf
  11.  http://www.theregister.co.uk/2014/12/29/german_minister_fingered_as_hackers_steal_her_thumbprint_from_a_photo/
  12.  http://www.sciencedirect.com/science/article/pii/S0921889014002632
  13.   S. Marcel, J. R. Millan. Person authentication using brainwaves (EEG) and maximum a posteriori model adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4):743–748, April 2007.
  14.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6199830&tag=1
  15.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7126357&tag=1
  16.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7314193
  17.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6994220
  18.  https://kar.kent.ac.uk/49535/1/PIN_ijcb.pdf http://www.inderscienceonline.com/doi/abs/10.1504/IJBM.2014.060960
  19.   http://www.researchgate.net/profile/Patrizio_Campisi/publication/282122165_EEG_Biometrics_for_User_Recognition_using_Visually_Evoked_Potentials/links/56039ec808ae08d4f171779c.pdf
  20.   http://www.sciencedirect.com/science/article/pii/S0925231215004725
  21.   http://link.springer.com/chapter/10.1007/978-3-319-19713-5_13#page-1
  22.  http://download.springer.com/static/pdf/440/chp%253A10.1007%252F978-3-642-27733-7_9145-2.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Freferenceworkentry%2F10.1007%2F978-3-642-27733-7_9145-2&token2=exp=1447987756~acl=%2Fstatic%2Fpdf%2F440%2Fchp%25253A10.1007%25252F978-3-642-27733-7_9145-2.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Freferenceworkentry%252F10.1007%252F978-3-642-27733-7_9145-2*~hmac=be7c889f4ec177bd4096057e765bb2169f3614dfd4323d2e28787a2e3b92393c
  23.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
  24.  http://www.ncbi.nlm.nih.gov/pubmed/19163618   

http://www.igi-global.com.proxy.libraries.rutgers.edu/gateway/chapter/full-text-pdf/7471

 

  1.   http://www.metaljournal.com.ua/assets/Journal/MMI-6/060-Jianfeng-Hu.pdf
  2.   http://csee.essex.ac.uk/staff/palaniappan/1930378.pdf
  3.   http://ieeexplore.ieee.org.proxy.libraries.rutgers.edu/stamp/stamp.jsp?tp=&arnumber=5634487
  4.   – Need to find citation for this one as well  
  5.   Auditory potentials – https://etd.ohiolink.edu/!etd.send_file?accession=ucin1439300974&disposition=inline
  6.   http://ojs.bibsys.no/index.php/NIK/article/view/243
  7.   http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7280344
  8.   http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7318985
  9.   Facial movements, eeg authentication using artifacts http://link.springer.com/chapter/10.1007/978-3-319-07995-0_34#page-1
  10.   Multi-level approach based on eye-blinking http://www.sciencedirect.com/science/article/pii/S0167865515002433
  11.   http://www.nature.com/neuro/journal/v18/n11/full/nn.4135.html
  12.   DOI is good  http://www.nature.com/nphoton/journal/v8/n6/full/nphoton.2014.107.html
  13.   DOI vs fMRI – http://www.ncbi.nlm.nih.gov/pubmed/23578579
  14.   http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1619442

 

Neuroscience Research Market

How valuable are anonymized and unaltered brainwaves?

According to various modern researchers and the news reports released by Grand View Research, Inc, the global neuroscience is projected to reach the value of USD 30.8 billion by 2020. During the forecast period, the pace of growing developments in the field of neuroinformatics and sudden rise in patented research initiatives supported and funded by governments are factors projected to be responsible for the drive in the market share.

In 2016, the estimated value of the global neuroscience research was at USD 28.42 billion and it is quite expected to grow at a CAGR of 3.1% over the period forecast. The factors responsible for influencing the price thereby propelling the market growth include brain mapping research and investigation projects, neuroscience-based initiatives by government bodies, and the advancement of the technological tools and algorithms that are considered for implementation in the neuroscience space.

Over the forecast period, the introduction of novel technologies serving the purpose of mapping neuronal circuits situated in the brain functions is expected to boost the growth of this market. Furthermore, as a result of the global geriatric population, there is a significant increase in the demand for neuroscience-based research as this segment of the population is more prone towards earning the high risk of central nervous system disorders such as Parkinson’s disease and Alzheimer’s thereby making the market growth highly propelled.

 

REFERENCES

The science behind outdoor advertising

https://www.grandviewresearch.com/industry-analysis/neuroscience-market

https://www.prnewswire.com/news-releases/neuroscience-market-size-to-reach-308-billion-by-2020-grand-view-research-inc-532637711.html

https://www.forbes.com/sites/hbsworkingknowledge/2013/02/01/neuromarketing-tapping-into-the-pleasure-center-of-consumers/#77ab9dec2745

The science behind outdoor advertising

 

Proof-of-Work Versus Proof-of-Cognition

Can a proof-of-work (POW) protocol substitute or supplement a proof-of-cognition (POC) protocol?

Can a proof-of-work (POW) protocol substitute or supplement a proof-of-cognition (POC) protocol? It is possible, but not ideal. Humans in a POC protocol have equal mining power, instead using human biology to secure human conscience. If mining power were unequal (as is the case with POW), human consciences could be manipulated a debatably far worse outcome than a simple double spend in a currency system. By relying on machines rather than biology, the network can be overpowered by artificial intelligence producing their own mining hardware, or re-routing existing mining power to reap digital currency rewards (BGP hijacking). Furthermore, miners in a POW protocol are motivated by currency rewards for honest mining. Human-to-human transactions would need to be made feeless if humans were to continuously verify each other. Determining which verification webs were human-based would be difficult or impossible from a POW miner’s perspective.

Can POC replace POW? The simplest reason it cannot is that miners in a POC protocol would be able to inject bad blocks into the network, sending themselves currency when they had previously had sent it elsewhere. Since POC trades a valueless data structure, this risk is nonexistent.

Proof-of-work is ideal for currency, while proof-of-cognition is ideal for identity.

Reference: proof-of-cognition-implants , published May 2015. Disclaimer: Project Oblio’s mechanism does not rely on brain implants, but the mechanisms of action are the same. An early version of the paper provably exists in bitcoin address 13eeMVU5fXNfZdoBk5z4fEAbgSH9MawQ6H.

Liveness-detectable biometrics, and cybersecurity

How can we use liveness-detectable biometrics to provide computational barriers to creating fake accounts and fake votes?

Network security today is more difficult than it needs to be, largely because it must be based on the assumption that a single user can hold multiple accounts or multiple IP addresses. The one commonality between e-mail spam, DDOS attacks, presidential election meddling, and blockchain fees is that they’re all based on a single person holding multiple accounts. This person may submit multiple forum posts, multiple http requests, or multiple high-fee cryptocurrency transactions for the sole purpose of attacking or clogging the network, ruining it for everyone else. You might know this just as “spam”, or as “fake news”.

If there were a walled garden of the internet where each user was guaranteed to have provably one account, based on their biometricity, just like in the real world. You could also ban users who spam the network with these inexpensive requests, and could hold each person’s posts to a higher degree of accountability (people wouldn’t submit fake news if they couldn’t shed their post’s biometric signature). In blockchain, the real reason blockchain payments aren’t cheaper than say Visa for millions of users is due to this fact; People are clogging the network with low-priority payments and spam payments.

hd1

So, building a biometric internet isn’t really that simple. You can’t just say “here’s my fingerprint, now create my account”, because a fingerprint is really just an image file. An attacker could copy the scan of your fingerprint and pretend to be you very easily, otherwise this would have been done before.

What you need is a signal that exhibits biometricity, unique features that identify you, but is also a liveness challenge, a puzzle that basically asks “are you human?” and “are you actually there, right now, submitting this biometric signal?”.  If what you submit equates to a yes, you can enter the garden. If you’ve ever had to type in numbers on a street sign, or crooked letters in all caps, or anything that’s like “I’m not a robot”, that’s a liveness challenge (Google’s reCAPTCHA). It’s a liveness challenge, but it doesn’t tie you to one account because it doesn’t exhibit biometricity. There’s nothing biological to distinguish your response from that over other users — it guarantees one person per computer, but it doesn’t guarantee you’re going to act honestly once you’re inside, because you’re still anonymous. A liveness challenge that also exhibited biometricity would ensure that whatever biometric signal you were submitting, like a fingerprint, wasn’t copy and pasted from a file, but rather the signal was generated quite recently, from an actual human being behind their computer screen.

A simple example of a challenge that exhibits both liveness detection and human detectability might be to have some sort of public ledger that is a string of random values, like a blockchain. Each block contains a random hash value that you can use to derive 20 random words out of a list of 1000. These words are derived from the hash value, meaning there is a relation between the block’s hash and this new, presentable stimulus. You could then present these 20 random words to a user and tell them, “quick, say these 20 random words out loud!”. A person says the words and their voice, which is unique to them, is recorded and propagated across the network, where it is confirmed by nodes to be a biometric unique to them. It’s a liveness challenge because it’s difficult for a computer program to generate audio that (1) exhibited biometricity and (2) contained words seeded by a very recent block (i.e. the computational power to generate this may expensive if the seeding block was found quite recently). The fact that you were able to say the words so quickly would indicate that the signal wasn’t generated in advance, or before the random value was submitted, which is a sign that the words came from a live human. A person’s voice is a good (but not great) biometric, so it exhibits biometric features as well.

hd2

Everything discussed so far is great, except for the fact that the NSA and big tech companies have been collecting voice data for years and would be really good at generating a voice simulation to say those 20 words within 3 minutes (or three seconds). We learned from the Titanic that nothing is ever going to be 100% secure, so voice data definitely has a role in our network, but as a more future-proof liveness challenge/biometric, imagine now you’re inputting an electrical signal that was generated based on a block hash. The signal passes over a user’s skin near their left ear and comes out their right ear. The signal as it leaves the right ear still contains elements of the original signal, but it has been modulated based on the unique biological properties of your skin. We then digitize the analog signal so that it can be transmitted to a network of computers which analyze it.

hd3

The most efficient blockchain will be one where every single user has provably one account. Decentralized systems are plagued by spam, so if you can unclog the network from these spam attacks, you can make the fees cheaper for everyone. Right now credit card companies take 2% off every transaction, a financial network with 0% fees is obviously going to be preferable; Money will always flow to the space with highest liquidity (unless it’s prone to corruption like EOS). To build this one-person-one-vote network you need a biometric that exhibits liveness, and one method for doing that is described here, using the unique properties of brainwave biometricities.

A long-term validation metric can be used as an appendage to this, as pioneered by OpenMined.org. Basically, to validate that data isn’t faked, a machine learning algorithm (over a time-consuming process) determines which data improves its recognition rates and which do not. If it were possible to fake out a machine learning algorithm with bad data, then ML theorists would already be doing it, and nobody would be paying for data. Thus, the most stable foundation for a network like this ultimately (and ironically) relies on artificial intelligence (in the long-term / for cementing transactions) as well as fluid biometrics and a reputation system (in the short-term / for immediate payouts).

Finally, we provide a financial incentive for identifying fake accounts based on biometric signals by creating a challengers-verifiers market. People are motivated by financial reward to check each biometric signal submitted and verified by block producers. Block producers are expected to submit fake data 1% of the time and distribute new oblio as a reward. See our github for the latest spec on this component.

Keep an eye out for our next post, where we’ll be delving into the fully-blockchain-compatible algorithm that reached higher identification rates on brainwaves than any other study we’ve seen.

How to Scale a Smart Contract Blockchain

The simplest solutions are often the best. Let's create a voting system to vote in good block producers, and kick out the bad ones, where everybody has exactly one vote.

Imagine if you were in a big, empty room with one or two other people. It would be very easy for each of you to communicate by shouting at one another from corner-to-corner. This is how blockchains used to be, back when very few people knew about them.

Imagine now you start adding more people to the room. Suddenly, communication relies on everyone shouting at each other in all different directions, and each person is overwhelmed with information. Nobody has time to understand anything that they’re hearing – the channels are clogged. How can you fix this problem?

 

Before you invest in any type of blockchain, you should understand what the cons are to blockchains themselves. When we talk about blockchains and scaling, it’s easy to misconstrue the facts, because just as there are many types of blockchains, there are many different viable ways to scale a blockchain as well.

For starters, “scaling” is just making a blockchain accessible to a lot more people – orders of magnitude more, in fact. On a decentralized network, everyone should be able to help out. But the more crowded a blockchain becomes, the harder it is for every person contributing to the blockchain to process new information related to supporting it. This, effectively, moves power to those with pre-existing wealth– those with more bandwith, more storage space, etc.

Going with the analogy described earlier, one solution to the ‘crowded room’ problem is to charge money before people are allowed to shout information. This makes sure only really serious people get to speak into the crowd. But, it also makes communicating (over blockchains) very expensive. People with a lot of pre-existing wealth can launch “spam attacks” on the network to drive up the fees on regular users’ transactions. If it’s a proof-of-stake chain or a centralized proof-of-work algorithm, then these rich persons are probably profiting in this attack, because they’re basically raising the fee price by paying huge fees while most of the fees go back to them anyway. As the network becomes clogged, its market price tumbles, and said “bag-holders” profit off their margin-traded short position. If we’re talking about a smart contract blockchain, this becomes a fundamental problem for developers too.

Experienced programmers can imagine the tiny, but not-so-negligible cost of running a website on one external server. It’s only about $5 per month to get a user database up and running on Amazon AWS. With dApps, things get a lot more expensive. Rather than only one user database, you’re effectively paying everyone on the network to calculate your program’s output at the exact same time, in addition to listening for basic communications. More computational power means higher fees. In the crowded room scenario, imagine if someone had to not only listen to transactions, but also type in each one’s instructions on a handheld calculator before they could process the next one.

 

Another solution to the crowded room is the “telephone game”. People at the corners of the room might whisper information towards central spokes near the middle. This is a bit like how Ripple works. It might also be an analogy for payment channels. But Ripple and payment channels don’t seem to apply to smart contracts, the part where people have to type stuff into calculators. This sucks, because smart contracts have proven to be incredibly useful. If we’re not only asking people to share information, but having each person do calculations on it, the “whisper method” really doesn’t play.

On Project Oblio, nodes that are “ordained”  run smart contracts in subgroups and tie their output to their own biometricity and staked wealth. The difference here is that rather than running the same program across many nodes (traditional smart contract chains), only a small group of ordained nodes need to run, verify, and sign a particular program’s output with their unsheddable biometricity. Because a person can’t shed their biometricity, one person can’t spin up more than one node to dupe the network (Project Oblio is one-person-one-vote, anti-Sybil system). Thus, this proof-of-individual system is a highly-secure way to evaluate smart contracts.  More importantly, it reduces fees by  requiring considerably less computational power.

The gist of it is, on wealth or work-based blockchains, a single smart contract needs to be run by every single node before a block is verified. On Project Oblio, a smart contract or “service” only needs to be verified by one or more trusted individuals to reach approval of the network.  This reduces fees, and allows for greater computational prowess than previously described.

As one eerily might have expected after reading Satoshi’s white paper, one-human-one-vote offers a lot more for society than one-CPU-one-vote. Only through one-person-one-vote  can you scale a smart contract blockchain.

The Supermoney Highway Needs On-Ramps

Can we build an on-ramp into blockchain that doesn't need a third-party to validate a user's identity?

We like to think of blockchain engineers as highway builders. We spend a lot of time worrying about the stability and scalability of our highway, and also how fast you’ll be able to go (send money), and what kind of vehicles (smart contracts) you’ll be able to drive. Unfortunately, we often forget about “on-ramps”, or ways to actually get on our highway. We forget how to get average people outside of tech to actually join our network.

Often, I speak with dApp owners who are only concerned with raising funds to cover technical costs. Generally, they want to operate their currency like a company, specifically by holding a pre-sale (or “ICO”) of an initial percentage of their currency, similar to seeking venture capital when starting a business. But currencies aren’t stocks. You can’t create a currency with an unbalanced initial distribution of wealth and expect people to want to join later on. Your goal should be to first build something desirable. Then, you should put your currency in the hands of as many people as possible. A highway is useless without drivers, and drivers can’t drive without gas.

 

In the U.S., the best way to get your hands on any cryptocurrency is to link your bank account to Coinbase, and wait 5 days for a bank transfer to convert your dollars to bitcoin. For the tech-averse or financially-privy, exposing personal details about one’s bank account to a third-party is a significant deterrent to adoption. Not only that, for a community that prides itself on limiting third-party risk, it’s somewhat unusual (and risky) to have one company tracking the financial records of nearly every professional crypto user involved.

 

There are other ways to obtain cryptocurrency aside from buying through Coinbase, but frankly they’re even more cumbersome. One way is by mining, but again this is not for the tech-averse. Another, newer way to earn currency is through a coin’s budget system. Typically every month, coins with a budget system will make a billboard list of tasks, such as coding or online marketing, that need to be accomplished by the end of the month. Users can sign-up to complete these tasks and if succesful, they receive cryptocurrency as a reward. In a future iteration of Project Oblio, every privileged person will be assigned one monthly task to accomplish.

 

On Project Oblio, one of the primary ways to receive value is by providing data from consumer EEG devices and/or Vybuds. The biometric aspect of this type of data ensures that the data is authentic, i.e., that it hasn’t just been copied and pasted from a previous submission.  This data has practical use cases within its features, including  neuromarketing and mental self-improvement.

 

Project Oblio is really a decentralized, crowd-sourced neuroscience experiment. Using data taken while performing tasks during EEG or tACS-like sessions, we can evaluate the effects of  leading neuroscientific techniques on improving memory, depression, and other mental ailments. Additionally, data that is recorded while watching TV shows, movies, or advertisements can be used to evaluate a user’s interest in cultural content or a product. This means musicians, filmmakers, and advertisers can have an objective metric over how much a user enjoyed their content. While review sites like IGN, IMDB, and Amazon often have third-party markets selling positive reviews, Project Oblio intends to have every review backed by spiritually-derived data. An experiment like this is the next obvious step for decentralized systems, because incentives for data can both improve a network’s quality while also laying out on-ramps for the average user.

 

When people want to join the “Information Superhighway” (better known as the internet), all they need is an internet-connected tablet, phone, computer, Alexa, or thousands of other devices made by thousands of manufacturers. When people want to join Project Oblio, they will need a consumer EEG or  a pair of Vybuds, which can themselves be used for tons of other things outside the network as well.  It might be the missing ingredient for a currency-like system to have an engine running off products that perform useful work for a user outside the actual currency system, so that a user can not only earn value with significant potential, but also better themselves. That is, for a currency to become popular, it should have goals of improving something other than the thickness of its initial investors’ wallets. It should have tenants that extend beyond the realm of corporate ideals.

Vybuds are ready to be manufactured, but venture capitalists don’t get it. We need money from the community to manufacture them!

Brainwaves as a Future-Resistant Biometric: Human-Detection, Identification, and Authorization

Brainwaves are the only biometric still untapped by governenments and corporations that possess fluid properties; Human-Detection, Identification, and Authorization. They are also a source of wealth.

“Human detection” is the act of proving whether or not an internet user is a robot, or a human. Google’s ReCaptcha2 (“I’m not a Robot”) is very convenient; It only takes about 10 seconds to complete. Unfortunately, the tasks required by Google’s reCaptcha are problematic for the following reasons:

  • These tasks are not future-proof. Eventually, A.I. will be smart enough to pass any image recognition task.
  • As Google trains their algorithms, they become the only ones with the algorithms capable of bypassing their own human detection protocols. They can then lease these algorithms to generate fake accounts. These fake accounts, paid for by a wealthy person or persons, or just a hacker, can be used to create a false sense of consensus on a discussion board regarding a government, product, or cultural content.

Project Oblio possesses a network component that is decidedly human-only. Transactions on this network can be decidedly human-only, as the network revolves around the idea that the most resilient form of human detection derives from the power of the human brain. Signals emanating from the brain and muscle areas around them are a cheap and efficient form of human detection, one that is vastly more future-proof that that currently implemented by Google.

Each of us is born with a multi-billion dollar supercomputer, capable of generating outputs immeasurably more complex than that capable of being understood by an A.I. For example, predicting whether a human will find something funny is much harder for A.I. than it would originally seem, even with plenty of data. This humor response manifests itself in an EEG recording, a piece of data that can simultaneously be conveniently monetized to the user on a decentralized network.  Common recording parameters like the P300 (a measure of whether a human brain has detected something “surprising”) are easily elicited over an EEG and probably just as difficult for a computer to simulate. Any form of human detection becomes significantly stronger when you’re simultaneously recording outputs from billions of live, biological neurons.

When we combine this type of human detection with transcranial direct current stimulation (tDCS: an “input” method, as opposed to EEG, which primarily records a brain’s output), we may get even stronger, faster human detection. Considering that tDCS has also been shown to improve memory, concentration, and relieve depression, it would seem to be the perfect technique for improving both inward and outward human communication.

Although non-invasive brain-machine-interfaces require a lot of data for most tasks we’d like them to be useful for (such as control of virtual reality), they are tremendously accurate at identifying us (like a “fingerprint”, but more accurate) with comparatively minimal data. While the more desirable tasks often have only 60-80% accuracy, identification of human beings by brainwaves can typically achieve 99% accuracy with minimal data. This is evidence that each brain and its corresponding outputs vary tremendously from person to person, and that collecting a lot of data from a single person is just as valuable, if not more valuable, than comparing data across persons. If you need more evidence for the uniqueness of human brains, check out the Human Connectome Project.

So. At this point we have two new terms: Human detection, and identification.It’s important now to realize that not only do human brains have tremendous power to human detect us, but they can simultaneously perform these two steps as well. No other biometric available can do this simultaneously, and it’s a crucial enhancement in a decentralized network’s stability. Fingerprints and DNA are both static – once you have the image or the code, it can be copied and used to impersonate you. Voice data is as fluid as brainwave data and could theoretically be used to human detect, but it is not as good at identifying you and is also (nowadays) easily forgable (see Alexa, Siri, etc.).  If data were easily foregable, a person could use the same data to receive multiple rewards, spamming the system and making themselves very rich (that is, until the markets crash due to the flaw they exposed in the network).

The last step in the three-pronged approach is authorization. While human detection is like reCaptcha, and identification is like a username, authorization is like a password. Authorization is as simple as you thinking a “password thought” recognized by a machine learning algorithm. There are tons of papers already out there about this, so I’ll point you to Google Scholar for this topic.

Combining these three methods: identification, authentication, and human detection, into a single protocol creates a triad of network security not found in other biometrics or in purely digital security. This is  interchangeably called “proof-of-person”, “proof-of-cognition”, “proof-of-humanity”, and “proof-of-individuality”. Its consequences are described further here.