failure family

privacy and surveillance risk

16 claims across 14 works.

• cross-border-node-storage-noncompliance: Blockchain creates a data protection paradox: the technology itself offers strong privacy protection, yet storing blockchain data across a global netw
• immutability-versus-erasure: The immutability and permanent recording built into blockchain technology may be the root of legal difficulties in European countries that recognize a
• cross-border-data-compliance-gap: Although blockchain technology itself offers genuine data and privacy protection, storing blockchain data across a global network of nodes often will
• cross-border-node-storage-noncompliance: Although blockchain technology itself offers unprecedented data and privacy protection, storing blockchain data across a global network of nodes often
• identity-exposure-of-producers: If block producers' identities are revealed, the supranational independence and security of the blockchain are threatened, because local jurisdictions
• translation-cost-overload: The cost of translating coded contractual intent into natural language has the potential to become overwhelming for the existing centralized legal inf
• broker-dealer identity disclosure: The Overstock structure fails to deliver investor privacy, because all existing broker-dealer customer agreements contain provisions allowing the brok
• IoT scale as trust liability: Growth in connected IoT devices is offset by falling consumer confidence, because the sheer number of devices creates unprecedented cyber security exp
• crisis ratchet in surveillance: Centralized authorities have a long record of using crises to justify the introduction of monitoring tools that outlast their original purpose.
• Identity disclosure weakening: Reducing member anonymity weakens rather than strengthens a decentralized network, because personal privacy protects members and lets them be more tra
• parameter exchange leakage: Federated learning does not eliminate privacy risk, because although the data stays decentralized the exchange of model parameters can still expose se
• parameter exchange leakage: Federated learning does not eliminate privacy risk, because although the data stays decentralized the protocol still exchanges model parameters, and t
• erosion of individual data control: As AI systems come to depend on vast data, personal information is converted from a resource the individual could control and deploy at discretion int
• decentralized audit opacity: Decentralized governance makes privacy compliance harder to demonstrate, because the distributed nature of these systems complicates tracking data flo
• Gradient leakage in federated learning: Federated learning does not eliminate privacy risk: because gradients and partial parameters are transmitted, the system remains vulnerable to attacks
• centralized data honeypot: Centralizing annotation data inside a small number of vendor firms creates a standing risk of breach or misuse that can produce legal liability and lo