Smart Contracts Management: The Interplay of Data Privacy and Blockchain for Secure and Efficient Real Estate Transactions
Bibliographic record
Abstract
The digital transformation of the real estate industry is being significantly influenced by blockchain technology and smart contracts, which promise enhanced efficiency, transparency, and security in transactions. This study aims to develop a secure and efficient smart contract management protocol that balances the benefits of blockchain with robust data privacy practices. The methodology involves descriptive analytics of transaction data from the Ethereum blockchain, feasibility studies using synthetic transaction data, and a regulatory compliance analysis to map the impact of different regions' regulations on blockchain adoption in real estate. The findings reveal that while smart contracts can automate various processes and reduce reliance on intermediaries, challenges related to data privacy and regulatory compliance persist. Higher privacy features in smart contracts are associated with increased execution costs, indicating a trade-off between privacy and cost efficiency. Smart contracts with privacy level 3 had an execution cost of 0.025 ETH, compared to those with privacy level 1 at 0.02 ETH. Integrating permissioned blockchains and zero-knowledge proofs offers a promising solution, though their complexity limits broader adoption. Zero-knowledge proofs maintained high privacy (achieving privacy levels of up to 0.76) at a reasonable computational cost (proof generation time of 1.9 seconds). Thus, the integration of permissioned blockchains and zero-knowledge proofs offers a promising pathway to address these challenges. However, the complexity of these techniques requires specialized knowledge, limiting broader adoption. The study concludes with recommendations to develop specialized training programs, collaborate on regulatory frameworks, invest in advanced cryptographic research, and implement targeted strategies to overcome adoption barriers. These efforts will contribute to the digital transformation of asset management, fostering innovation and enhancing the overall efficiency of real estate transactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".