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How Blockchain and Machine Learning are Altering the Mobile Privacy Landscape

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source link: https://blogs.sap.com/2021/08/31/how-blockchain-and-machine-learning-are-altering-the-mobile-privacy-landscape/
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August 31, 2021 3 minute read

How Blockchain and Machine Learning are Altering the Mobile Privacy Landscape

Blockchain is rapidly developing into one of the most disruptive and widespread technologies across a range of industries today. Not only does it have the potential to revolutionize the finance and healthcare landscape, but it may also combine with machine learning to drastically alter privacy and security for mobile devices. 

Although it’s often been looked upon as the complimentary technology behind Bitcoin, blockchain has become a solution with far more widespread potential. Key players in the world of tech have all developed their own competitive suites of blockchain services – showing that the world’s most innovative companies are beginning to wake up to the potential that the technology holds. 

It’s actually the Bank of America that holds the most blockchain patents, with the financial interest in the tech made clear from the number of institutions exploring blockchain. 

The potential that blockchain holds is massive. But despite this, there are still many obstacles for the technology to overcome on the path to widespread adoption. Primarily, blockchain must first demonstrate its ability to protect the personal data of its users at scale – in the face of growing threats to online security – before it becomes a global disruptive force. 

The secure infrastructure of blockchains has made it an essential part of the growing cryptocurrency ecosystem, but it has plenty of uses beyond digital finance because of the immutable ledger that blockchains create that makes the technology virtually watertight in cases of attacks. 

The ledgers behind blockchains mean that they can be used to prove the authenticity of anything that it tracks – helping to establish greater levels of trust between users and the companies that they share their data with online. But how can this work to keep our information secure whilst browsing on our mobile devices? 

Generating Trust

The power of blockchain and machine learning is such that some cybersecurity experts believe that the technology has the power to fully revolutionize the way we keep our information safe. One day, blockchain and ML is expected to develop to the point where the use of usernames and passwords that are commonplace for security online will be replaced by new technology, with users being provided instead with their own personal encrypted identities that can be used automatically to manage all of their accounts. 

Blockchain has the ability to track and store your private data safely owing to the immutable nature of the ledger, which is distributed across a range of nodes that are fully distributed across the network. Because there is no central store of data – and because changes need to reach a consensus across nodes – it’s extremely difficult for criminals to change the information within a block to steal money or commit fraud. 

Transactions are securely recorded on blockchains. These distributed ledgers aren’t controlled by any central authorities, making them fully independent and thus safe from manipulation from central authorities. 

This means that in the future, we can keep our personal accounts safe online by possessing our own immutable form of identification to access our information and to make changes to personal data, sign confidential documents, and make purchases online. Machine learning will support this by learning when an automated sign-in is required to make the process entirely frictionless. 

The Case for Privacy

When building a blockchain for the purposes of protecting privacy, one of the key issues that arise comes from how different privacy and protection can be from one another. Data protection is heavily oriented around securing data from prying eyes, whilst privacy refers to defining access to personal data. 

Data protection is a highly technical topic, whilst the matter of privacy is more legal – and they certainly don’t go hand-in-hand. Privacy refers to the user’s rights, and protection is the responsibility of the party gathering the data – or the user themselves. Both are vital for online security, but blockchain technology is particularly excellent at protection. 

As blockchains can’t be manipulated illegally, it’s impossible for users to erase their history from a blockchain. However, this can lead to legal issues in terms of the EU’s GDPR regulations, which states that all users have the right to be forgotten. The immutable nature of blockchain is great from a security perspective but problematic in the face of current privacy regulations. 

This, however, does make blockchain an ideal player in the future of mobile transactions, as the technology can bring greater confidence to those wishing to make purchases using their smartphones. 

As our work and leisure time becomes more remote in the wake of Covid-19, the notion of keeping our devices secure at all times – whether they’re on local or public WiFi networks – is more imperative than ever. In response to this, many users have looked to VPNs in order to secure their data, and the combination of blockchain technology and machine learning is likely to help to make the development of virtual private networks more comprehensively secure in the future. 

The Future of Mobile Privacy

We live in an interconnected world that’s constantly on the go. This means that mobile privacy technology will become essential over the coming years, and the necessity grows as criminals discover new technology and frailties to exploit. 

The next decade will be significant in the fight against cybercrime. As our dependence on smartphones continues to grow, the necessity for online safety becomes increasingly essential. In the emergence of blockchain, our digital identities can help to provide us with swift, secure, and untamperable access to our data without the risk of it falling into the wrong hands.


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