Iagons Multiple Blockchain Support

IAGON aim at providing its users and miners complete flexibility and freedom of choice in providing and consuming decentralized cloud services . Hence, IAGON will provide a multiple Blockchain solution. running its cloud storage and processing operations both on the Ethereum Blockchain and on Tangle.

Users and miners can choose either Ethereum or Tangle to fully securely store their files, to process computational tasks , to pay and to receive IAGON tokens for cloud services , and primarily to benefit from huge advantages in gaining access to the market’s prominent and state-of-the-art technologies.

Iagon’S Secure Lake Technology

The Big Data market is characterized by the recent adoption of Data Lake architectures, such as information systems that are based on the Hadoop framework, by large companies. The Data Lake architecture is based on implementation of a NoSQL central database (such as MongoDB, HBase or Cassandra) in which files of any sort can be stored and be retrieved from.Companies can virtually define a central depository for their information and data files that does not depend on the contents oron the file types and provides a user -friendly and accessible source for allthe files managed either in SMEs, middle sized companies orlarge corporations.

None the less, the data lake architecture suggests that once it is hacked, an intruder can “swim” in the database system, explore the files and gain access to valuable data describing every aspect of the operations of an organization that is hacked. One of the major uses of IAGON’s Secure Lake technology in encrypting, slicing and distributing the data lake files is “freezing” the lake, that is prohibiting by means of encryption and decentralization of files any party from navigating within the data lake after gaining access to it (see Figure 3).

Hacking a Data Lake of any organization exposes it to unlimited number of security, privacy and financial risks, from online publication of private information of clients, through use and sale of suppliers and commercially sensitive data to trading trade secrets, internal correspondence and digital goods (such as source code and designs of new products).

The vulnerabilities as well as the hacking possibilities of databases of Big Data and Data Lake infrastructure are publicly posted online, mainly warning organizations against security breaches that may rise due to use of these platforms.

Few examples from the recent years illustrate the broad scope of threats and risks to organizations (as well as to their customers and suppliers) that result from hacking their IT systems and databases:

  • In January 2017,Camarda (2017) reported that"Hadoop attacks followed ongoing attacks on MongoDB, ElasticSearch, and Apache CouchDB. In some cases, criminals have been know to clone and wipe databases, claiming to hold the originals for ransom. In other attacks, they have simply deleted databases without demanding payment".
  • At the same period, Constantin (2017 )reported that “It was only a matter oftime until ransomware groups that wiped data from thousands of MongoDB databases and Elasticsearch clusters start ed targeting other data storage technologies ... 126 Hadoop instances have been wiped so far. The number of victims is likely to increase because there are thousands of Hadoop deployments ac cessible from the internet although it’s hard to say how many are vulnerable . The attacks against MongoDB and Elasticsearch followed a similar pattern . The number of MongoDB victims jumped from hundreds to thousands in a matter of hours and to tens ofthousands within a week . The latest count puts the number of wiped MongoDB databases at more than 34,000 and that of deleted Elasticsearch clusters at more than 4,600.”
  • Claburn (2017) indicates that the actions of the attackers on Hadoop based systems “may include destroying data nodes,data volumes,or snapshots with terabytes of data inseconds”.
  • Earlier reports explain how to hack into Hadoop systems and to exploit their vulnerabilities to destroy of copy large volumes of data (see for example Gothard, 2015). Given the nature ofthe vulnerabilities exposed , and those that have not yet been exploited by attackers , but may exist in the systems , as well as the lack of policies of ongoing cyber security auditing in many organizations , databases at large are exposed to other parties , should they decide to apply these intrusion techniques . The results for any organization can be catastrophic and have a large magnitude of impact on its operations . To illustrate ,the Equifax hack ,reported in September 2017, exposed the personal data of143 million customers, causing a daily fall of19% inEquifax’smarket value.

IAGON’s Secure Lake is based on the Blockchain unbreakable encryption technology, on file slicing and storage of small, anonymous and strongly encrypted slices ofthe original files ensures the complete protection of data files, other types of files (such as scans, photos and videos) and databases of any size and ensures the rapid retrieval and update of any stored file. Except from the user who securely uploads a file and has the password (key)to retrieve and encrypt it, no one can read the contents ofthe small file slices, encrypt, delete, change ,retrieve them, identify their source or even associate them with other file slices that are generated from the original , uploaded file . IAGON ’s technology ensures that even when information systems are breached in anyway,the data and files that they use cannot be accessed, deleted or modified in anyway.

Iagon’s Smart Computing Grid Platform And Ai-Tracker Technology

The increasing demand for processing poweris evident for example by the growing sales of NVIDIA systems for Machine Learning and Deep Learning operations , aswell as other advanced operations of Artificial Intelligence that require vast volumes of computing and processing capabilities . The technology domain of AI based innovations that require large capacities of processing power(mostly supplied by batteries of serverswith large amount of CPUs and GPUs) include face recognition , video processing , voice analysis ,text analysis , pattern recognition in Big Data databases and digital document repositories , autonomous cars, IoT based decision support systems and many more.AI technologies and applications are expected to exponentially grow overthe next years ,thereby increasing the demand for processing power to support both research and their day-to-day operations.

IAGON’s Smart Computing Grid is equivalent to any other power grid (such as solar production of electricity)

  • It connects multiple producers to customers
  • Smart Computing Grid fulfils the demand for the necessary resource
  • It transfers unused resources to customers in need (CPU and GPU processing power and storage space), and
  • It benefits the miners providing processing power and storage space to the grid without requiring efforts when their servers and computers are not used by them.

The Smart Computing Grid is based on advanced Artificial Intelligence components that include more than 100 Machine Learning algorithms ,methods and techniques that integrate to form our AI-Tracker system.AI-Tracker is the “brain ” behind IAGON ’s Smart Computing Grid . It optimally allocates encrypted file slices to the miners ’ free storage spaces and computational tasks to the miners ’free (idle)CPUs and GPUs that compose the Smart Computing Grid.

AI-Tracker is a dynamically learning system that continuously analyzes past and current data streams that reflect the availability of storage space and processing capacities ofminers.AI-Tracker carries outthe tasks of optimally allocating and transmitting encrypted file slices to designated storage spaces , allocation for processing tasks for rapid , optimal performance ofthe grid and identification ofrogue nodes that should be blocked and removed from the grid and continuously fine tuning the grid ’s attributes to optimize its performanceat any time(seeFigure4).