What is blockchain technology trusted third parties
Distributed ledger technology — commonly known as blockchain, possesses the potential to transform industries by automating transactional and administrative processes. It starts with the assessment of the value of this technology for your company or organization. If the following conditions apply, then blockchain technology has a strong potential to provide value:. Multiple participants share and update data — multiple parties need views of common information and take actions that need to be recorded. One or more parties have more or better information than the others, This creates an imbalance of power and can cause the transactions to go awry.
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- NoPKI - a Point-to-Point Trusted Third Party Service Based on Blockchain Consensus Algorithm
- Blockchain’s technology of trust
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- Will External Audits Vanish in the Blockchain World?
- Blockchain is cryptonite for many industry leaders
- Blockchain: The New Technology of Trust?
- Blockchain: The Potential and Pitfalls
- UN/CEFACT Conference on "Blockchain"
- How to Start a Career in Blockchain
NoPKI - a Point-to-Point Trusted Third Party Service Based on Blockchain Consensus Algorithm
Try out PMC Labs and tell us what you think. Learn More. The protection of private data is a key responsibility for research studies that collect identifiable information from study participants. Limiting the scope of data collection and preventing secondary use of the data are effective strategies for managing these risks. An ideal framework for data collection would incorporate feature engineering, a process where secondary features are derived from sensitive raw data in a secure environment without a trusted third party.
This study aimed to compare current approaches based on how they maintain data privacy and the practicality of their implementations. These approaches include traditional approaches that rely on trusted third parties, and cryptographic, secure hardware, and blockchain-based techniques.
A set of properties were defined for evaluating each approach. A qualitative comparison was presented based on these properties. The evaluation of each approach was framed with a use case of sharing geolocation data for biomedical research. We found that approaches that rely on a trusted third party for preserving participant privacy do not provide sufficiently strong guarantees that sensitive data will not be exposed in modern data ecosystems. Cryptographic techniques incorporate strong privacy-preserving paradigms but are appropriate only for select use cases or are currently limited because of computational complexity.
Blockchain smart contracts alone are insufficient to provide data privacy because transactional data are public. Trusted execution environments TEEs may have hardware vulnerabilities and lack visibility into how data are processed. Hybrid approaches combining blockchain and cryptographic techniques or blockchain and TEEs provide promising frameworks for privacy preservation.
For reference, we provide a software implementation where users can privately share features of their geolocation data using the hybrid approach combining blockchain with TEEs as a supplement. Blockchain technology and smart contracts enable the development of new privacy-preserving feature engineering methods by obviating dependence on trusted parties and providing immutable, auditable data processing workflows.
The overlap between blockchain and cryptographic techniques or blockchain and secure hardware technologies are promising fields for addressing important data privacy needs. Hybrid blockchain and TEE frameworks currently provide practical tools for implementing experimental privacy-preserving applications. The emergence of social networks, smartphones, wearable devices, and internet of things IoT devices introduces unprecedented avenues for the mass collection of personal data about behaviors, biology, and health.
The ubiquity of these technologies presents novel challenges when considering how to protect the privacy of individuals, and the potential to reveal sensitive and identifiable information intentionally or unintentionally has grown.
A recent Pew Research Center report found that physical location data represent one of the most sensitive data types [ 1 ]; yet more than popular smartphone apps track precise location data, some of which sell that data to third parties for targeted ads or analytics [ 2 ]. Although location companies claim that the data collected are used to analyze aggregate patterns, not individual identities, employees and clients still have access to raw data and could identify users without their consent.
Major telecommunications carriers sell user location data, and reporters have shown that data can be resold to a long chain of downstream companies. The lack of regulation in this data ecosystem has resulted in a black market for the sale of user location data [ 3 ]. Once a third party collects user data, it is difficult to guarantee that the data are not misused or mishandled.
Between and , Cambridge Analytica collected social media data from Facebook users for academic research, but later repurposed the data for political advertising [ 4 ]. In the past decade, major data breaches have exposed billions of user accounts [ 5 ].
There are also several instances of malicious apps that directly expose private information without user consent [ 6 ]. These issues present difficulties for biomedical researchers conducting studies that would otherwise benefit from convenient, passive, and longitudinal methods of data collection to identify novel biomarkers and develop digital therapeutics.
There is a need for an open and trusted method for sharing data with untrusted third parties that ensures 1 posterior privacy, where personal data are not shared beyond the study for which the individual has consented and 2 that the data are only used for the intended purpose of the study. In this paper, we reviewed the current state of privacy-preserving techniques for personal data, motivated by a location-sharing use case with applications in health care.
We compared privacy-preserving techniques along several axes, including the level of trust required in the research team, the generalizability of the technique, and the availability of open source tool support. It is our intention to provide a pragmatic road map to help researchers make informed decisions about the utilization and processing of sensitive personal data. We provide a reference software implementation for the location-sharing example use case, using one of the examined techniques for privacy preservation.
Smartphone phone usage, and geolocation data in particular, is consequential for several health care applications.
Location data have already been used in a variety of applications in health, for example, to monitor behavioral and environmental risk factors [ 8 , 9 ], to improve disease management and treatment delivery [ 10 ], and to inform public health policy in substance abuse [ 11 ]. In a representative example, researchers found that features extracted from global positioning system GPS; movement and locations and phone usage social connectedness strongly related to symptom severity in depression.
The availability of smartphone tools provides a vector for continuous, passive assessments that could one day augment current data collection methods in clinical psychopharmacology [ 12 ]. However, it is important to stress that although geolocation data can be valuable for health care research, it is also one of the most fundamentally sensitive pieces of personal information. Feature engineering is the process of transforming raw data into a representation that is amenable to machine learning algorithms.
For example, say you are building a system to forecast driving time between two locations in a major metropolitan area. You are given data that contain the date, time of day, and driving time between the two locations for the previous year.
The raw date data YYYY-MM-DD are unlikely to be useful for predicting drive time, but knowing whether the day is a weekday or weekend may be very useful. A machine learning scientist might write code that returns true if the date is a weekday and false if it is a weekend.
The newly engineered Boolean feature, weekday , encodes important domain knowledge—that traffic patterns are different on weekdays compared with weekends—and may improve the accuracy of the predictions from the machine learning model. Historically, feature engineering has been a manual process, based on the experience and domain expertise of the machine learning scientist [ 13 ].
More recently, automated systems that learn feature representations automatically from the data, such as sparse coding and auto encoders, have demonstrated good performance as the basis for deep learning models. Here, we describe a framework for feature engineering that preserves the privacy of identifiable data and is applicable to either manual or automated feature engineering procedures.
Our approach is based on the premise of minimal exposure; that participants should only reveal the minimal data required for the study and researchers should only collect the data required for the study.
The feature engineering step of an analysis pipeline offers an opportunity to limit exposure by transforming identifiable, sensitive, or otherwise private data into deidentified or anonymized features. This minimal exposure approach to feature engineering creates a framework that benefits both participants and researchers.
By making it openly difficult for researchers to obtain raw personal data, participants may feel more willing to share their data and contribute to research studies. At the same time, removing researcher data access may simplify and expedite research studies by reducing the resources diverted toward maintaining secured data servers and limiting exposure to personally identifying information. In Figure 1 , we illustrate the approach whereby raw data and feature extraction are encapsulated in a secure environment, removed from the researchers who are primarily interested in the underlying features.
A minimal exposure approach to feature engineering, where sensitive raw data are not exposed to a third party. As an example, reverse geo-encoding is performed in a secure environment to extract a location category, which could be used to determine population models on prescription refill adherence.
This suggests that one of the more popular applications of blockchain technology centers around the idea that individuals may desire control of their data as a way of feeling that their privacy and data are kept more secure. A blockchain consists of a distributed network of unaffiliated computers nodes that maintain an immutable record of transactions that are verified using a cryptographic protocol.
Blockchain networks are further characterized as public, private, or consortium networks depending on who can participate in the network, and how transactions are verified.
In public blockchains, transactions are verified and a global state of truth distributed ledger is maintained by a trustless network. A trustless network refers to a decentralized network with a consensus protocol. The consensus protocol incorporates sender authenticity via public key cryptography, game theory and cryptoeconomic digital currency incentives, and computational complexity to ensure that honest nodes are rewarded, and dishonest nodes are penalized to maintain the canonical truth.
By making each transaction auditable and permissionless, public blockchains ensure data integrity, trust, and verifiability. Advances in blockchain technology have enabled the deployment of rule-based, self-executing software code called smart contracts.
Smart contracts remove the need for intermediaries by acting as predefined arbiters. In addition, smart contracts are immutable and publicly verifiable when the contract code is made public. The combination of smart contracts with a trustless environment is what eliminates the need for trusted third parties that are responsible for managing private data.
These features make smart contracts particularly relevant to this study. The aim of this study was to examine and compare current privacy-preserving methods based on their ability to maintain the privacy of personal shared data. Methods were compared based on the level of trust required of a third party, and the practicality of implementing these techniques framed as a feature engineering step. This study also aimed to identify the more promising techniques that researchers and software developers can use when building applications concerned with preserving data privacy.
The examination is set against a practical use case of collecting location data from individual participants, from which interesting features related to health can be extracted.
To make this example as accessible to researchers as possible, we provide an open-sourced software project that implements one of the examined techniques for the location sharing use case. Define a set of properties on which to evaluate the privacy-preserving properties of each approach. A qualitative comparison, grounded in a geolocation feature engineering use case, of the privacy-preservation properties of each approach.
A proof-of-concept software implementation for extracting the category of a location from GPS coordinate data while maintaining privacy using one of the more practical blockchain techniques.
We conducted a review of literature, health care—related blockchain use cases, and applied blockchain projects on the Web. These techniques were identified using keyword searches in electronic databases Google Scholar and PubMed and search engine Google results.
The keywords were privacy blockchain , deidentification , and privacy feature engineering. The results at the time of the search January consisted of methodologies described in a variety of formats, including 4 academic papers in peer-reviewed journals, 6 academic papers in conference proceedings, 2 literature and product surveys, 1 doctoral dissertation, 7 scientific journal preprints, 11 product specifications, and 1 academic lecture materials.
The techniques were divided into the following categories: 1 methods that rely on a trusted third party, 2 cryptographic methods, 3 trusted execution environments TEE , and 4 methods incorporating blockchain. Examples about existing implementations of each of these technologies are included in Multimedia Appendix 1 [ 15 - 43 ]. Data privacy laws [ 44 - 46 ] offer a regulatory perspective on the several dimensions in which data privacy can be compromised.
Table 1 summarizes some of the key regulatory principles. These regulatory guidelines make it clear that data privacy is highly dependent on the responsibilities of trusted organizations, and the capabilities of the technologies they implement. We predict that future data-sharing systems will be informed by these privacy guidelines and that a framework for evaluating privacy-preserving technologies should map to these guidelines.
In this paper, each privacy-preserving approach is evaluated based on the following properties:. Like most complex data types, GPS data are typically transformed before being used in an analysis through feature engineering.
There are 2 broad classes of geolocation features that underlie most of the current health care research applications of geolocation data.
They compute summary statistics from the raw GPS data. For example, total distance traveled in a day, the variance in number of locations visited, and the travel radius.
They combine the GPS data with a third-party geospatial information system to determine location types, such as library, gym, or house of worship or broad location themes eg, neighborhoods with high rates of crime defined by census data.
A few examples of application use cases that would use geolocation features include replacing active monitoring tasks [ 8 - 10 , 47 - 49 ], triggering just-in-time interventions [ 10 , 49 , 50 ], and accessibility to health services [ 11 , 51 ].
Geospatial applications that incorporate blockchain include the management of IoT devices, crowd-sourced data collection, and emergency response [ 52 ]. Reference to geolocation feature extraction will be made in the Results and Discussion sections to ground the investigation in a practical use case while evaluating different approaches for preserving privacy.
In a traditional biomedical research setting, the protection of human subjects is managed by an institutional review board IRB at the research institution. The role of the IRB is to certify that research subjects are informed of the risks of participating in research, data security guidelines are followed, and risks and safeguards are clearly outlined and mitigated. In this model, the research institution operates as a trusted third party with a responsibility to protect patient data privacy.
Blockchain’s technology of trust
Already an IBA member? Sign in for a better website experience. Heap is also an innovator who is fed up with the middlemen of the music industry making money out of, and at the expense of, the artists who actually make the music. When she released her track, Tiny Human , in , she did so through Ujo Music, a platform connected to blockchain technology. Through Ujo Music, Heap is able to directly control everything to do with her track, including holding onto the rights and intellectual property, without the need for any middlemen. We use banks, governments, credit card companies, even platforms such as Airbnb, which acts as an intermediary between prospective guest and prospective host. The intermediary verifies identities and uses authorisation processes to ensure that assets are actually transferred.
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You have likely heard about blockchain technology through its association with cryptocurrencies such as Bitcoin. However, while cryptocurrency remains an important application of blockchain technology, it is only one example. Blockchain has emerged from the once-shadowy world of cryptocurrency to become a transformational technology for many businesses. Companies such as IBM, Microsoft, and JP Morgan Chase are making huge investments in blockchain that they hope will make operations more efficient and give them a competitive advantage. With many companies seeking talented blockchain professionals, there has never been a better time to pursue a career in the field. This blog will outline a career pathway to becoming an in-demand blockchain professional. Blockchain is the latest version of distributed ledger technology DLT that helps people do business with each other by tracking who owns what.
While millions of people worldwide rely on online sharing activities, such services are often facilitated by a few predatory companies, managing trust relations. This centralization of responsibility raises questions about ethical and political issues like regulatory compliance, data portability and monopolistic behaviour. Recently, blockchain technology has gathered a significant amount of support and adoption, due to its inherent decentralized and tamper-proof structure. We present a blockchain-powered blueprint for a shared and public programmable economy.
Will External Audits Vanish in the Blockchain World?
Many online applications, especially in the financial industries, are running on blockchain technologies in a decentralized manner, without the use of an authoritative entity or a trusted third party. Such systems are only secured by cryptographic protocols and a consensus mechanism. As blockchain-based solutions will continue to revolutionize online applications in a growing digital market in the future, one needs to identify the principal opportunities and potential risks. Hence, it is unavoidable to learn the mathematical and cryptographic procedures behind blockchain technology in order to Hence, it is unavoidable to learn the mathematical and cryptographic procedures behind blockchain technology in order to understand how such systems work and where the weak points are. The book provides an introduction to the mathematical and cryptographic concepts behind blockchain technologies and shows how they are applied in blockchain-based systems.
Blockchain is cryptonite for many industry leaders
A decade after its launch, blockchain is still the only internet-age technology that is able to facilitate online trust using mathematics and collective protocolling exclusively. The buzz around blockchain — from its initial release a decade ago until today — stems from the fact that it was the first technology to establish trust online via mathematics and collective protocolling alone. At Ericsson, we see significant value in blockchain technology as a trust enabler and potential disruptor that can enable completely new business models in the digital asset market. This Ericsson Technology Review article explains blockchain technology from an Ericsson perspective and shares the key insights we have gained from a number of promising private blockchain use cases we have evaluated both on our own and together with global telco and enterprise customers. The article also discusses potential future developments and business considerations.
Blockchain: The New Technology of Trust?
Blockchain: The Potential and PitfallsRELATED VIDEO: The blockchain technology be used to replace trusted third parties such as companies holoc
Blockchain technology is at the heart of cryptocurrencies like Bitcoin. A database is a place where information is stored electronically. Databases are used everywhere in the real world—including banks, which use them to store information about accounts and transactions. But not all databases work in the same way. In some databases, the information can be changed or edited by a central authority, or by anyone with permission to do so. So, when new information is added, a new block is created rather than an old one being edited.
UN/CEFACT Conference on "Blockchain"
In order to understand Blockchain and its interest, we have to beforehand understand the essential role of trust. Trust results in a set of determinants : security and reputation. In that case, the intermediary materializes itself as a digital platform of services and it get paid per se like banks or notary do it. It is also a characteristic of trust : it has a price, and value. With the globalization and the increasing volume of transactions, the use of trusted third parties has become more costly, like it has become longer and less efficient due to the difficulty to draw the complicated chain of operations and of different actors into the modern logistical and financial circuits.
How to Start a Career in Blockchain
Banks, finance, estate agents and a whole range of businesses must think again. This technology has the potential to change the ways we engender trust, thereby influencing our relationships with agreements, trade and ownership. Players within banking and finance are now being threatened by small companies with disruptive blockchain technology. Blockchain is best known as the technology behind the digital currency bitcoin, which has already led to innovation among central banks all over the world.