How to model user trust in data sharing

🔐 User-Centric Credentialing & Personal Data Sharing: Rethinking Data Ownership and Digital Trust I came across a powerful concept that’s redefining how we think about data and identity, while exploring Digital Public Infrastructure (DPI) and Government Digital Transformation. That is User-Centric Credentialing & Personal Data Sharing — a vision spearheaded by Centre for Digital Public Infrastructure - CDPI and already being adopted in countries like India, Brazil, and across the EU. 📄 You can read the full vision paper here: https://vc.cdpi.dev/ 🎯 The Problem: Most of our data—academic records, financial info, medical history—sits locked in institutional silos. Whenever we need to prove something, we must go back to those institutions, again and again. This system is inefficient, exclusive, and often inaccessible to those without digital privilege. 🔄 The Shift: Instead of relying on fragile paper documents or non-verifiable PDFs, Verifiable Credentials (VCs) allow individuals to receive cryptographically signed, tamper-proof data directly from the source—and hold it themselves. Your credentials live in a digital (or even printable) wallet, ready to be presented anywhere, anytime. 🧩 Why this matters: 🚫 No more redundant verification loops or complex API integrations 💸 Individuals and SMEs can unlock low-cost, high-trust access to loans and services 🌐 Cross-border, cross-sector data sharing becomes truly scalable 🔐 Privacy-preserving tech like selective disclosure and zero-knowledge proofs lets users control what they share 💼 Real-World Use Cases: 🚜 Farmers accessing government subsidies 🎓 Students applying for global jobs or education 🛒 Micro-entrepreneurs seeking credit 🌱 Green energy prosumers trading surplus power 🔧 How it works — The Technology: ✅ Verifiable Credentials (VCs) Issued by trusted institutions (banks, hospitals, universities) Tamper-evident and cryptographically signed Verifiable without contacting the issuer Works online, offline, and across borders 🌍 Decentralized Identifiers (DIDs) Globally unique, user-owned digital identifiers Enable selective disclosure and zero-knowledge proofs Not tied to any centralized registry or country 🧠 The Architecture: Trust Without Friction 🟩 Issuer → signs and issues the credential 🟦 User → stores it in a wallet (smartphone, cloud, or paper with QR) 🟨 Verifier → verifies it cryptographically, without needing the issuer again This model eliminates the need for bilateral system integrations. Just one connection: the user. It’s asynchronous, scalable, and privacy-respecting. 🌐 Why this matters for the future: 📲 Anyone, even without advanced tech access, can participate 🛠️ Institutions issue once and never worry about re-verification 🔐 Built on open standards, decentralized architecture, and zero-trust principles Ministry of Digital Economy - Sri Lanka Information Communication Technology Agency of Sri Lanka

𝐇𝐨𝐰 𝐜𝐚𝐧 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫𝐬 𝐛𝐮𝐢𝐥𝐝 𝐭𝐫𝐮𝐬𝐭 𝐭𝐨 𝐜𝐫𝐨𝐰𝐝𝐬𝐨𝐮𝐫𝐜𝐞 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐝𝐚𝐭𝐚? Access to users’ platform data is critical to conducting impactful research on topics like privacy or unfair business practices. Yet data is siloed and it is frequently impossible for researchers to get access. My colleagues Alex Berke, Dana Calacci, and I tried a new approach: crowdsourcing. We asked 6,000 individuals for their personal background and 5 years of amazon purchase histories. We published this dataset (with full consent from participants) to enable new research on e-commerce. Along the way, we learned key lessons about crowdsourcing data, now published in the ACM, Association for Computing Machinery Conference on Computer Supported Cooperative Work: 🔍 Transparency matters. Telling people why we were asking for their data and showing them their collected data boosted share rates. We could reduce monetary incentives by more than half while maintaining similar share rates by simply showing participants the data we were going to collect. 🔐The privacy paradox is real. We present one of the largest studies of the “privacy paradox”: the disconnect between people’s actual willingness to share personal data and their stated reluctance to do so. The impact of monetary incentives was six times higher when real money was on the line than when people were asked about hypothetically sharing their data. 📊 Support for Research. While most participants opposed government use of purchase data, most supported researchers accessing it for the public good. Check out the full paper below to learn more!

Recent client meetings have left me a bit stumped! Because I keep hearing the following: “We don’t trust our data.” It's not the first time I've heard it, and I bet it won’t be the last. The irony? Those same businesses were using data every single day to pay invoices, run supply chains, and make strategic calls. So it’s not really the data they mistrusted. It must be something deeper. So where does this mistrust come from? Sometimes it’s a cover for not liking what the numbers say (because numbers don’t bend to opinion). Other times, it’s really about trust in the data team rather than the data itself. Occasionally, it’s just become a lazy throwaway line. If organisations want to break this cycle, both leaders and data teams need to change the way they work together. Here’s a 5 point playbook that stops “data mistrust” in its tracks: 1. Define Once, Use Everywhere: agree common definitions for key metrics. Document them, make them visible, and hold teams accountable for sticking to them. Consistency builds confidence. 2. Show the Journey: make data lineage transparent. Leaders should see where a number originates, how it’s transformed, and why it ends up in a dashboard etc. Traceability removes suspicion. 3. Shared Accountability: data isn’t an “IT product.” It’s a joint effort. Business leaders must own the accuracy of inputs; data teams must own the quality of models and outputs. Co-ownership prevents finger-pointing. 4. Resolve Issues Quickly: don’t let data concerns fester. Implement visible feedback channels, track issues openly, and close them with clear communication. The faster issues are addressed, the stronger trust becomes. 5. Normalise Hard Truths: not all insights will be comfortable. That’s the point. Leaders must be ready to hear what the numbers say, and data teams must present them clearly. Data itself isn’t untrustworthy. It’s the behaviours, mindset, and responses around it that determine whether people believe it. So let’s stop hiding behind the lazy phrase “we don’t trust our data.” 👉 Business leaders are you really questioning the data, or just avoiding what it’s telling you? 👉 Data teams are you giving the business clarity, speed, and confidence, or just more numbers to argue over? Because until both sides stop passing the blame, “data mistrust” won’t go away, it will just keep undermining decisions. Mark Stouse Bill Schmarzo Malcolm Hawker Eddie Short Kyle Winterbottom Edosa Odaro Joe Reis Matthew Small Dan Everett