Data sharing between businesses has always been an issue. The primary reason for this is the lack of trust between organizations. Businesses like to ensure that their markets are not negatively affected by sharing sensitive corporate data, which acts as a set-back to data sharing. Data ownership is nowadays perceived as equally important to owning oil or physical assets. Therefore, many businesses consider the confidentiality of their datasets as a critical factor that affects their ability to maximize their market share through their current data-driven business practices. In this context, individual businesses seek benefits from data sharing only when they can do so without letting their competitors on the game. Furthermore, there are also cases where companies are reluctant to share data with their business partners because they afraid that this could put their customers’ privacy and security at risk.
These are certainly valid concerns when companies share data on public portals or deploy poor cybersecurity measures. Nevertheless, it is no longer true that data sharing cannot be safe. Recent advances in data-sharing infrastructures and privacy preserving technologies (e.g., privacy preservation computation) make it possible for businesses to securely share data with their business partners in various supply chains where they participate. Such technologies are already used in a variety of use cases that are based on the sharing of sensitive business data, such as use cases in the areas of digital finance, healthcare, and industry.
The emergence of new data sharing technologies is gradually changing the way companies think about data sharing in business-to-business (B2B) settings. Here are some of the emerging technologies that make the difference:
Except for these technologies, there are also advances in tools and techniques for preserving the privacy of customers and other end-users. Specifically, modern organizations can use privacy preserving computation techniques to overcome privacy and data protection challenges. These techniques help companies to analyze data in a way that ensures confidentiality while still driving business insights and innovation. They come to alleviate the ever important inherent conflict between data sharing and privacy protection, which stems from the fact that companies have no control over who has access to their data.
Privacy Preserving Computational Techniques enable privacy friendly analysis of different datasets in ways that do not expose the source data, but only the results of the analytics and the queries. They do this by leveraging cryptographic protocols for secure computation, which allow computation on encrypted inputs without revealing either the inputs or the interim results of computations on them. This enables privacy friendly analysis of different datasets in ways that do not expose the source data.
Privacy preserving computation approaches enable companies to share data with their partners in order to gain insights into their own business, or access to new opportunities. From a technological perspective, privacy preserving computation uses a cryptographic technique that is called ‘homomorphic encryption’. The latter allows computations on encrypted data without revealing anything about the underlying plaintext data. This makes it possible for two parties to collaborate on a project without ever sharing any sensitive information.
The execution of homographic encryption computations is very demanding in terms of computational resources. Once upon a time, enterprises were not offered with cost-effective access the compute cycles needed to implement non-trivial privacy preserving computations. This has recently changed with the broad availability of large amounts of compute cycles at an affordance cost.
While there are multiple challenges facing B2B data sharing and analyses, there is also an increased focus on developing solutions for these challenges. Organizations that implement solutions for B2B data sharing initiatives will be able to improve their business processes and gain competitive advantages over others which do not invest in this area. Specifically, companies must bear in mind that open sharing of data is a key to harnessing the true potential of big data in the data economy. This is the reason why enterprises must seriously consider establishing a successful data sharing infrastructure that will help them share data with their business partners in effective ways and with guaranteed data security.
Neuro-Symbolic Learning Explained
The First Insights on ChatGPT and Generative AI Impact on Productivity
Tools and Techniques for Data Quality Assessment
Top 5 Data Science programming languages
Machine Learning as a Service (MLaaS): The basics
Trading Data as NFTs: The basics you need to know
Active (Machine) Learning: Leveraging Human Experience to Improve AI
Digital Platforms for a Circular Economy
AI Regulatory Initiatives Around the World: An Overview
We're here to help!
No obligation quotes in 48 hours. Teams setup within 2 weeks.
If you are a Service Provider looking to register, please fill out this Information Request and someone will get in touch.
Outsource with Confidence to high quality Service Providers.
If you are a Service Provider looking to register, please fill out
this Information Request and someone will get in
Enter your email id and we'll send a link to reset your password to the address
we have for your account.
The IT Exchange service provider network is exclusive and by-invite. There is
no cost to get on-board;
if you are competent in your areas of focus, then you are welcome. As a part of this exclusive