Homomorphic Encryption Vs. Multiparty Computation

Bleeding edge encryption techniques allow one to monetize their personal health information without sacrificing privacy.

Encryption:

Encryption is the conversion of information into a code unreadable for unauthorized users by the use of an algorithm. Encryption is important when you don’t want anyone else to have access to your private data, such as brainwave data, selfie video data, or personal health data. There are many ways to compute (do math on) the encrypted data without knowing whose information it is about out of which, two are: Homomorphic Encryption and Multiparty Computation.

Homomorphic encryption is a kind of encryption in which the data is converted into a ciphertext which can be later analysed and worked on as it was still in its original form. The ciphertext is an encrypted version of the input data. It is operated on and then decrypted to obtain the desired output. This encryption allows us to perform complex mathematical operations on encrypted and secured data. It transforms one data set into another without harming the relationship between elements of both sets.

Multiparty computation is used to evaluate the inputs of two or more parties while keeping their inputs hidden from each other. This is done when different parties wish to jointly compute a function to their inputs in such a way that there certain security properties are preserved.

In simpler term, encryption allows us to hide data in a way that appears meaningless to anyone except those who have access to the secret decryption key. 

 

PROTECTION OF DATA THROUGH HOMOMORPHIC ENCRYPTION:

There have been many attempts to secure genomic privacy of biologically researched data using cryptographic methods. Particularly, it has been suggested that the privacy can be protected through homomorphic encryption.

The math on brainwave data recorded, of secret participants, using EEG while watching TV commercials, can be done through homomorphic encryption without decrypting the data.

The companies that get the brainwave data, never want to reveal the identity of their participants, that is why they send the samples in an encrypted form, to the lab, where the computations are done using homomorphic encryption, and the predictions (results) are sent back, to the company, in the encrypted form; where only they can decrypt it back using decryption keys. In this way, the identity of the person is never disclosed. The data is encrypted, also because companies and labs are bound by regulations and participant’s agreements to handle his data confidentially.

PROTECTION OF DATA THROUGH MULTIPARTY COMPUTATION:

Multiparty computation can be implemented using different protocols, such as Secret Sharing, in which the data from each party is divided and computed on separately. Then after combining again, it provides the desired statistical results. Security in multiparty computation means that the players’ inputs remain secured (except for the results that are computed) and the results computed are correctly. The security is supposed to be preserved In the face of any sort of adversary. Intuitively, no party learns about any other party’s inputs.

All in all, computation of encrypted data is an interesting topic that explains how cryptography faces the hardest problem of protecting data in use. This is just an overall review about what these two methods of computation have to offer us. The past few years have seen the most significant advances in making the use of these two technologies on more wider-scale.

 

For more information, see the project at OpenMined.org

 

 

References

BUILDING SAFE ARTIFICIAL INTELLIGENCE. (n.d.). Retrieved from open mined: https://www.openmined.org/

Computing Over Encrypted Data. (2017, may 29). Retrieved from Enigma: https://blog.enigma.co/computing-over-encrypted-data-d36621458447

homomorphic encryption. (n.d.). Retrieved from Search Security: https://searchsecurity.techtarget.com/definition/homomorphic-encryption

Homomorphic Encryption Market Size, Worldwide Analysis, Design Competition Strategies, Company Profile, Development Status, Opportunity Assessment and Industry Expansion Strategies 2027. (2018, june 7). Retrieved from 14 News: http://www.14news.com/story/38373250/homomorphic-encryption-market-size-worldwide-analysis-design-competition-strategies-company-profile-development-status-opportunity-assessment-and

Private predictive analysis on encrypted medical data. (2014, Aug). Retrieved from Science Direct: https://www.sciencedirect.com/science/article/pii/S1532046414000884

Secure multi-party computation made simple. (2006, Feb 1). Retrieved from Science Direct: https://www.sciencedirect.com/science/article/pii/S0166218X05002428

 

 

What is neuromarketing?

Neuromarketing enables one to substantially profit off the multi-billion dollar super-computer between their ears.

Neuromarketing is the science about your customer’s minds. It includes the direct use of brain imaging, scanning or other brain activity measurement technology to predict the subject’s response towards specific products, packaging or any other marketing element. It is the application of neuroscience to marketing.

Every year, billions of dollars are being spent on advertising campaigns. But conventional techniques have failed to predict how a customer feels when he is exposed to an advertisement. Neuromarketing offers cutting edge methods to know how a customer’s brain actually works and what effect does marketing have on the consumers’ population.

Neuromarketing researchers believe that consumers sometimes make subconscious decisions in a split of second. They believe that consumer’s decision can be driven through changing their emotions.

How Neuromarketing Works:

Knowing how an advertisement captures a consumer’s attention is what neuromarketing is all about. Research data is gathered by using certain biometrics that include:

  1. Eye tracking: tracking eye movement to understand which part of the advertisement is most appealing to the viewer.
  2. Facial coding: Testing facial expressions to learn certain responses about a product or an advertisement.
  3. Skin response and electrodermal activity: measures sweat gland secretions and different levels of excitement and arousals.
  4. Electroencephalography (EEG): measures electrical activity in the brain which is linked with increased or reduced focus and/or excitement levels.

Use Of EEG in Neuromarketing:

EEG biosensors makes the neuromarketing research easy. This allows the researchers to record consumer’s response in the right place such as movie theatre, bars etc. The biosensors can be placed on the head that accurately measure the brain activity of the subject. The changes in the electrical activity of the brain determines the emotional response of the person being tested, also whether he is engaged in watching the advertisement or not at all focused.

EEG can also reveal that the consumer was very attentive during the first 30 seconds of the advertisement but lost interest in the last 30 seconds. This feedback, in, turn, could better help in making the last 30 seconds of the advertisement even more effective.

Earning Money by Watching Advertisements and Using EEG:

This has also become a popular way of making money. This simple yet very beneficial tool can be used as a source of earning by using EEG to record your brainwaves while watching TV commercials and advertisements at home. People get paid by different companies and brands for selling their brainwave data. The general price paid per brainwave recording is between $80 and $100 per hour.

Different companies recruit people and pay them for watching their advertisements and recording their brainwaves by using electroencephalogram scans. It records on a second-by-second basis regarding how people respond to the commercial.

Neuromarketing has helped marketers make engaging and effective commercials. This not just benefits the marketers but the customers as well in enhancing their experience with a brand or product long before they consider buying it. This field is gaining unbelievable popularity among marketing and advertising professionals and is growing day by day.

 

 

References

Making Ads That Whisper to the Brain. (2010, nov 13). Retrieved from https://www.nytimes.com/2010/11/14/business/14stream.html: https://www.nytimes.com/2010/11/14/business/14stream.html

Neuromarketing and EEG: Measuring Engagement in Advertising. (2018, june 14). Retrieved from NeuroSky: http://neurosky.com/2016/08/neuromarketing-and-eeg-measuring-engagement-in-advertising/

Neuromarketing: Marketers scan consumers’ brains to test their ads. (2015, nov 5). Retrieved from CBC: http://www.cbc.ca/news/technology/neuromarketing-brainsights-1.3303384

Neuromarketing: The New Science of Consumer Behavior. (2011, jan 14). Retrieved from springer link: https://link.springer.com/article/10.1007/s12115-010-9408-1

What is Neuromarketing? (n.d.). Retrieved from Neuromarketing: https://www.neurosciencemarketing.com/blog/articles/what-is-neuromarketing.htm

 

 

 

Alternative Modalities in BMI Biometrics

While most medical applications of the EEG BMI require clean data, recorded artifacts from facial movements and eye-blinks have been shown to be exceedingly accurate in classifying individuals.

Historically, when a subject undergoing an EEG recording session blinks or twitches their face, a sharp spike appears on the recording trace that renders that epoch unusable. While most medical applications of the EEG BMI require clean data, recorded artifacts from facial movements and eye-blinks have been shown to be exceedingly accurate in classifying individuals.

In addition to facial movements, eye-blinks are now being integrated with EEG systems to achieve a higher classification rate than using brainwaves alone. In [34], it was discovered that using a cheap, consumer-version EEG headset (the Neurosky Mindwave) achieved a 99.4% classification rate by discriminating eye-blinks in addition to EEG brainwaves. This represents the highest classification rate for a significant subject size (31 persons) to date using an EEG instrument.  Eyeblinks may have some disadvantages relative to brainwave patterns (such as forced-volition and lack of continuous verification), and are likely to be more prone to mimicry, but may be easier to train. Integrated with the continuous verification protocols offered by EEGs, eyeblinks may provide a high-level of security when requested in discretized intervals.

An exciting new modality analyzes differences in neural connectomes derived from functional magnetic resonance imaging (fMRI) to identify individuals. Unlike EEGs, fMRI relies on imaging deep neuronal structures, often in a clinical setting. The neural connectome is a diverse, complicated web of linkages among neurons inside the brain, which varies both with experience and genetics. In [35], connectomes constructed in the Human Connectome Project identified persons in a group of 126 persons with high accuracy (approximately 93%). It was noted that analyzing just the frontoparietal network was more distinctive across subjects compared to analyzing the whole brain (p < 10-9), and that these ‘neural fingerprints’ did not change when a person was at rest or performing a task. A person’s fMRI data from one session was used to create a connectivity matrix of their neural connections. This connectivity matrix was then compared to all other connectivity matrices from the remaining sessions, and a correlation coefficient for each session was calculated.

Disadvantages of using fMRI as a biometric is that the subject must remain stationary, it is contraindicated in subjects with implanted metallic devices, and it is significantly more expensive than other methods of biometric verification. However, conclusions drawn on fMRI data are likely to hold true on developing mobile technologies, such as DOT (Diffuse Optical Tomography) [36]. DOT has a greater temporal resolution than fMRI, allowing for continuous verification. However, it may be more dependent on detector placement than EEG [37].

 

Citations:

  1.   Facial movements, eeg authentication using artifacts http://link.springer.com/chapter/10.1007/978-3-319-07995-0_34#page-1
  2.   Multi-level approach based on eye-blinking http://www.sciencedirect.com/science/article/pii/S0167865515002433
  3.   http://www.nature.com/neuro/journal/v18/n11/full/nn.4135.html
  4.   DOI is good  http://www.nature.com/nphoton/journal/v8/n6/full/nphoton.2014.107.html
  5.   DOI vs fMRI – http://www.ncbi.nlm.nih.gov/pubmed/23578579
  6.   http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1619442

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

 

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.