Facebook’s algorithm, I learned, isn’t flawed because of some glitch in the system. It’s flawed because, unlike the perfectly realized, sentient algorithms of our sci-fi fever dreams, the intelligence behind Facebook’s software is fundamentally human. Humans decide what data goes into it, what it can do with that data, and what they want to come out the other end. When the algorithm errs, humans are to blame. When it evolves, it’s because a bunch of humans read a bunch of spreadsheets, held a bunch of meetings, ran a bunch of tests, and decided to make it better. And if it does keep getting better? That’ll be because another group of humans keeps telling them about all the ways it’s falling short: us.
How Facebook’s news feed algorithm works. (via onaissues)
« We asked some of our favorite artists to visually define some ideas and genres we felt had a big presence in 2015. Rob Corradetti, a.k.a., Killer Acid, injects mind-twisting style into the world of brain wave art by outfitting the cranial organ with an EEG scanner. With a portfolio that ranges from album artwork for the Black Lips, to Wiz Khalifa, and even Mac Demarco, to multiple satirical comic book series, Killer Acid has drawn a lot of brains. Now they can talk. »
« Here are some brain wave artists who popped in 2015. + Lisa Park + Peter Crnokrak + Scott Chasserot + Connection Codes + Brian Foo + Behnaz Farahi + Aiste Noreikaite »
Text graphics by Manfred Schroeder at Bell Labs in 1968. For the cover of the exhibition catalogue for Some More Beginnings organised by Experiments in Art and Technology (E.A.T.) at the Brooklyn Museum. source, h/t: Tim Koch
Neurodata and Neuroprivacy: Data Protection Outdated?
Abstract
There are a number of novel technologies and a broad range of research aimed at the collection and use of data drawn directly from the human brain. Given that this data—neurodata—is data collected from individuals, one area of law which will be of relevance is data protection. The thesis of this paper is that neurodata is a unique form of data and that this will raise questions for the application of data protection law. Issues may arise on two levels. On a legal technical level, it is uncertain whether the definitions and mechanisms used in the data protection framework can be easily applied to neurodata. On a more fundamental level, there may be interests in neurodata, particularly those related to the protection of the mind, the framework was not designed to represent and may be insufficiently equipped, or constructed, to deal with.
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Hallinan, D., P. Schütz, M. Friedewald and P. de Hert 2014. Neurodata and Neuroprivacy: Data Protection Outdated? Surveillance & Society 12(1): 55-72.
App Stores for the Brain: Privacy & Security in Brain-Computer Interfaces
Abstract — An increasing number of Brain-Computer Interfaces (BCIs) are being developed in medical and nonmedical fields, including marketing, gaming and entertainment industries. BCI enabled technology carries a great potential to improve and enhance the quality of human lives. It provides people suffering from severe neuromuscular disorders with a way to interact with the external environment. It also enables a more personalized user experience in gaming and entertainment.
These BCI applications are, however, not without risk. Established engineering practices set guarantees on performance, reliability and physical safety of BCIs. But no guarantees or standards are currently in place regarding user privacy and security. In this paper, we identify privacy and security issues arising from possible misuse or inappropriate use of BCIs. In particular, we explore how current and emerging non-invasive BCI platforms can be used to extract private information, and we suggest an interdisciplinary approach to mitigating this problem. We then propose a tool to prevent this side-channel extraction of users’ private information. This is a first step towards making BCI-enabled technologies secure and privacy preserving.
Excerpts
IV. PRIVACY AND SECURITY ISSUES IN NEURAL ENGINEERING
B. Neurosecurity
In 2009, Denning et al. [21] recognized that “the use of standard engineering practices, medical trials, and neuroethical evaluations during the design process can create systems that are safe and that follow ethical guidelines; unfortunately, none of these disciplines currently ensure that neural devices are robust against adversarial entities trying to exploit these devices to alter, block, or eavesdrop on neural signals”. Potential security threats that can be mounted against implanted neural devices were identified, and the term “neurosecurity” was introduced as “the protection of the confidentiality, integrity, and availability of neural devices from malicious parties with the goal of preserving the safety of a person’s neural mechanisms, neural computation, and free will” [21].
C. Brain Spyware - BCI-enabled Malicious Application
At the 2012 USENIX Security Symposium, Martinovic et al. [31] presented the first malicious software designed to detect a user’s private information using a BCI. They referred to is as the “brain spyware”. The authors used a commercially available BCI to present users with visual stimuli and record their EEG neural signals. They focused on the P300 response, and analyzed the recorded signals in order to detect users’: (a) 4-digit PINs, (b) bank information, (c) months of birth, (d) locations of residence, and (e) if they recognized the presented set of faces.
While the authors of [31] have focused only on the P300 response, it is not hard to imagine brain spyware applications being developed to extract private information about users’ memories, prejudices and beliefs, but also about their possible neurophysiological disorders. Currently, there does not seem to exist a way to resist these attacks. Moreover, recent results [28] show that attempts at willful deception can themselves be detected from an individual’s neural signals. Going a step further, the same authors [28] show that non-invasive brain stimulators, emitting imperceptible DC electrical currents, can be used to make a user’s responses noticeably slower when attempting to lie.
Thus, there is a growing need to address the potential privacy and security risks arising from the use of BCIs, in both medical and non-medical applications. As a first step, we are exploring which components of the EEG signal can be used to infer private information about a user, and quantifying the amount of exposed information.
V. THREAT MODEL
Consider an example model of an attacker who uses BCIs to extract private information about users. We assume this will involve non-invasive BCI devices, mostly intended for consumer use. Manufacturers of non-invasive EEG-based BCIs generally distribute software development kits and guides with their products, as well as technical support. Their intention is to promote application development, but such “open-development” platforms may compromise user privacy and security, since there is currently no review process, standards and guidelines in place to protect users, nor technical protection to restrict inappropriate or malicious BCI use.
As depicted in Figure 1, a typical BCI system consists of three main components: (R1) an acquisition system, (R2) an application, and (R34) a signal processing system, where the signal processing system consists of (R3) feature extraction and (R4) decoding (translation) algorithm components. The existing BCI open-development platforms typically grant every application developer full control over all four of these components. For the discussion of this paper, we will assume an attacker has an access to all of these resources (R1)–(R4). We next consider how an attacker uses these resources to develop malicious applications.
A. Types of Attackers
In Figure 2, two types of attackers are shown (as described in the caption). We distinguish between these types based on the way an attacker analyzes recorded neural signals. The first type of an attacker extracts users’ private information by hijacking the legitimate components of a BCI system. Such an attacker exploits for malicious purposes those feature extraction and decoding algorithms that are intended for the legitimate BCI applications. The second type of an attacker extracts users’ private information by adding or replacing the legitimate BCI components. Such an attacker implements additional feature extraction and decoding algorithms, and either replaces or supplements the existing BCI components with the additional malicious code. As can be observed from the Figure, the difference between the two attacker types is only in the structure of the “brain malware” component.
B. Methods of Extracting Private Information
We consider scenarios where an attacker interacts with users by presenting them with specific sets of stimuli, and recording their responses to the presented stimuli. In the current literature, there are several well-established methods of presenting stimuli to users:
• Oddball paradigm - a technique where users are asked to react to specific stimuli, referred to as target stimuli, hidden as rare occurrences in a sequence of more common, non-target stimuli [23].
• Guilty knowledge test - a technique based on the hypothesis that a familiar stimulus evokes a different response when viewed in the context of similar, but unfamiliar items [44].
• Priming - a technique that uses an implicit memory effect where one stimulus may have an influence on a person’s response to a later stimulus [39].
We assume an attacker can use any of these methods to facilitate extraction of private information. In addition, an attacker can present malicious stimuli in an overt (conscious) fashion, as well as in a subliminal (unconscious) way, with subliminal stimulation defined as the process of affecting people by visual or audio stimuli of which they are completely unaware [15]. Ways of achieve unawareness typically include reducing a stimulus intensity or duration below the required level of conscious awareness.
C. Examples of “Brain Malware” Information Misuse
Private information about BCIs users, extracted using “brain malware”, may be of interest to multiple parties, those using it for greater good and potential improvement of the quality of humans lives, but also to those using it to increase their own (financial) gains, as well as those using it simply to harm others. One can easily imagine the following examples of concerning BCIs use:
Example 1: As exemplified in Farahaney’s work [22], an access to an individual’s memories and emotional responses might be used by police enforcement and government agencies during criminal investigation, as well as for crime and terrorism prevention.
Example 2: BCI-recorded neural signals may be used in a variety of entertainment and relaxation applications. A person’s emotional response and satisfaction/annoyment level may, for example, be used to provide better (more accurate) music and/or movie recommendations. Similarly, information about a person’s activity and anxiety levels may be used to tailor a more personalized training routine or a relaxation session.
Example 3: Personal information, extracted from neural signals, could also be used for targeted advertisement, where in addition to (or instead of) information about a person’s activities on the Internet, an advertiser/retailer would have a realtime access to a person’s level of interest, satisfaction, or frustration with the presented material.
Example 4: On the other end of the spectrum, however, the extracted information about a person’s memories, prejudices, beliefs or possible disorders could be used to manipulate a person or coerce her/him into doing something.
Example 5: Finally, the extracted neural information could also be used to cause physical or emotional harm to a person. Examples of such actions have already been observed in the literature. Denning et al. [21], presented the case of individuals who placed flashing animations on epilepsy support webpages, eliciting seizures in some patients with photosensitive epilepsy.
VI. THE NEED FOR A COORDINATED PREVENTION APPROACH
Issues arising from misuse or inappropriate use of BCI technology most likely do not pose a critical concern yet, considering their limited use outside of research communities. However, existing and emerging privacy and security threats may be viewed as an attack on human rights to privacy and dignity [13]. Thus, they deserve immediate attention and careful consideration. We suggest that methods to prevent and mitigate BCI-enabled privacy and security threats must be developed now, in the early design phase. Doing so will allows us to keep up with Privacy-by-Design [1] values, as well as with general values of privacy-enhancing technologies.
We view the development of prevention and mitigation tools as an interdisciplinary effort, involving neuroscientists, neural engineers, ethicists, as well as legal, security and privacy experts. The first step of this interdisciplinary approach should be an open discussion, aimed at answering the following questions: (i) Who all should be allowed an access to individuals’ neural signals? (ii) Which components of these neural signals should those entities have an access to? (iii) How noisy, distorted or distilled should these components be made before making them available? (iv) Which purposes are the entities allowed to use the neural signals for? and (v) What are the risks associated with the misuse of the provided components, i.e., what amount of private information can be extracted from the provided components?
We expect the answers to questions (i)–(v) will lead to a “triangle” approach towards enhancing privacy and security of BCI technology. On one vertex of the triangle, we expect to have legal experts and ethicists, defining a set of laws and policies to govern legal use of neural signals. As an example of possible legal intervention, the law could examine should the BCI platforms indeed be immunized for the apps they sell, or is some other balance between manufacturers and application developers more appropriate for BCI technologies.
On the second vertex, we expect to have a group of neuroscientists and engineers, in charge of developing and establishing a set of industry and research standards, methods, processes and practices for secure and privacy-preserving BCI systems. One such practice may, for example, require there to exist a centralized authority in charge of reviewing and validating every BCI application before allowing its use in general population. Finally, at the third vertex we expect BCI systems manufacturers and application developers, developing, implementing and using engineering practice, methods and tools, in order to prevent and mitigate specific classes of security and privacy attacks. Clearly the IEEE, and its standards process, could have a role here.
Read more »
BONACI, T., CALO, R., & CHIZECK, H. J. (2014). App stores for the brain: Privacy & Security in Brain-Computer Interfaces. 1-7.
(classical) Brain-Computer Interface attempts to use brain signals to drive suitable actuators performing the actions corresponding to subject’s intention. However this goal is not fully reached, and when BCI works, it does only in particular situations. The reason of this unsatisfactory result is that intention cannot be conceived simply as a set of classical input-output relationships. It is therefore necessary to resort to quantum theory, allowing the occurrence of stable coherence phenomena, in turn underlying high-level mental processes such as intentions and strategies.
Human Enhancement and the Law: Regulating for the Future
7-8 January 2016, St Anne's College, University of Oxford
Keynote Speakers
Dr Jan-Christoph Bublitz
Jan-Christoph Bublitz is a researcher and lecturer in criminal law and legal philosophy at the University of Hamburg who specialises in cognitive enhancement.
Professor Bert-Jaap Koops
Bert-Jaap Koops is researcher at Tilburg University with a particular interest in the intersection of technology and regulation. His main areas of interest include cybercrime, cyber-investigation, privacy, and data protection.
Professor Anton Vedder
Anton Vedder is an Associate Professor at the University of Tilburg whose research focuses on the regulation of emerging technology and bioethics. His particular areas of interest include the acceptance and adoption of new technology, legitimacy, privacy, security, and transformations in health care.
Professor Ruud ter Meulen
Ruud ter Meulen is Emeritus Professor at Bristol University and Director of the Centre for Ethics in Medicine, specialising in justice in health care, the ethics of research, the care of the elderly and in evidence-based medicine.
Professor Karen Yeung
Karen Yeung is Professor of law at King's College London, specialising in regulatory government and emerging technology.
Dr Hannah Maslen
Hannah Maslen is Lecturer in law and philoshophy at New College, Oxford. She works at the Oxford Martin Programme on 'Mind and Machine', focusing on the legal, ethical and social implications of brain intervention technologies.
Streaming, torrent, warez des termes qui vous sont surement familier. Des mots qui résonnent à quiconque « s’intéresse » au piratage. Mais le piratage loin d'être un simple pillage organisé est avant tout une culture.
De la culture du piratage au piratage de la culture.
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