The origins of blockchain as many are familiar with it today can be traced back to the Bitcoin whitepaper, first published in 2008 by Satoshi Nakamoto, which offered a vision of a new financial system underscored by cryptography and trust in code.
Throughout the past decade, iterations of this technological infrastructure have gradually built out a diverse industry ecosystem, allowing for use cases that extend beyond cryptocurrencies and peer-to-peer transactions. From smart contracts to asset tokenization, across industries ranging from gaming, supply chain, Internet of Things (IoT), real estate, and many others, the proliferation of use cases across verticals are a testament to the technology’s inherent versatility.
Yet, while projects remain fixated on addressing calls for mainstream adoption and enterprise implementation, existing infrastructural flaws continue to hinder these efforts. Beyond the criticisms directed at today’s open source, decentralized, public blockchains pertaining to scalability, there’s also the matter of privacy.
In most public chains today, transactions and on-chain data exchanges are fully visible to all nodes in the whole network, allowing for greater auditability and transparency. Across industries where the sharing of sensitive data is crucial, this transparency comes at a cost, posing a critical risk that far outweighs the benefits.
The tipping point of transparency
Amid the throes of digitalization, the importance of data protection schemes cannot be overstated. Throughout the years, the rise of data as a bonafide asset has led to its prominence as a key driver of economic growth across a myriad of sectors.
Historically, whether internally or to external third-parties, data has been shared across centralized networks, leaving systems vulnerable to devastating breaches and security risks. To mitigate concerns pertaining to misuse, legislators have looked to stringent regulations and privacy compliance frameworks. Though regulations are a starting point, they certainly fail to address fundamental infrastructural weaknesses.
In turn, blockchain provides an alternative, with decentralization as an added security measure that eradicates that threat of a single point of failure across a distributed network.
Simultaneously, the immutability of these permission-less networks preserves the provenance of data shared on the network, mitigating risks of tampering. As companies, big and small, transition to blockchain in the hopes of benefiting from efficient data sharing and ease in information transfer, the question of privacy in blockchains is often overlooked, forgotten amid demands for greater transparency and accountability.
To address this, many projects are now looking to employ privacy-preserving mechanisms on their infrastructures, ranging from non-interactive zero-knowledge proofs (zk–SNARKS) to encryption algorithms such as secure Multi-Party Computation (MPC). In aggregate, these technologies encrypt data as it’s shared and only reveal the specific elements that are pertinent to a specific purpose.
A collective force
Data can be perceived as a historical record of behaviors – human, mechanical, or otherwise. From the personal details we voluntarily input into forms, to driving patterns transmitted from ride-sharing vehicles to train driverless cars, or the GPS coordinates transmitted from our phones to servers, millions of data points are transmitted every day, each producing an ongoing trail of activity. In the age of automation, these granular details play a critical role in optimizing mechanized processes such as those in machine learning, strategic decision making, and identifying valuable behavioral patterns.
In healthcare, for example, optimization is a key benefit derived from data collaboration. One can see this in the training of artificial intelligence systems in order to make more precise diagnoses or the treatment of rare diseases with trained algorithms. In these cases, large quantities of sensitive data such as electronic medical records provide valuable insights for research.
Take a scenario where a hospital does not have sufficient data to perform sophisticated healthcare research using machine learning – a hospital would have the ability to access a larger pool of data if they could access it from other healthcare providers. With privacy-preserving encryption algorithms, the aggregation of patient information from several hospitals can be used to make a “collective” calculation without revealing the inputs from any hospital or the raw patient data.
This means that the information needed to conduct medical research is made available, and may be processed algorithmically to produce a result, without revealing it to anyone. With blockchain, this computational output could be transmitted over the network and users could access this with the necessary assurance that it has not been tampered with.
On the other hand, risk mitigation and fraud prevention is a prime benefit for the financial services sector, where industry-specific data privacy requirements often hinder the benefits of seamless data sharing. A potential use case would be in credit-rating investigations where there exists no current global standard for which institutions are responsible, ranging from third-party agencies to central banks. However, these institutions have a fragmented financial data profile of a given individual and often require additional information from other banks, leading to a lengthy, drawn-out process.
With a decentralized, blockchain platform equipped with privacy-preserving mechanisms, raw data can be securely encrypted by mathematically-provable cryptographic algorithms that are sent for cross-checking and computation – only the outcome of that computation would be shared and seen on the network, allowing banks to freely exchange only the relevant data required. These mechanisms help to encourage data collaboration across banks and other financial institutions efficiently and securely, without fear of unpredictable human factors that could potentially impact how the data is used.
The next chapter
With an increased emphasis on connectivity to allow for real-time optimization, risk mitigation, and personalization, data collaboration now serves as the backbone of today’s digital economy. While the advent of blockchain has largely improved the way in which information systems are able to share and transfer data, privacy concerns continue to stand in the way of maximizing the full potential of data exchanges. As incidents of misuse continue to shape public discourse and consumer confidence, a growing sentiment of distrust will only continue to fester unless critical changes are made on an infrastructural level.
The introduction of privacy-preserving mechanisms will ultimately result in benefits for businesses and users alike. Cost-efficiencies are gained as everyday processes are advanced and optimized, leading to a better understanding of consumer needs. Simultaneously, the reinvigoration of trust and value in the consumer-corporate relationship will come to underscore a greater emphasis on data ownership and sovereignty.
As we look to a future where data and privacy go hand in hand, we’ll come to see a modern data marketplace underscored by equitability and trust that has the potential to unlock new possibilities that enhance multiple areas of our everyday lives.