A Time-Lock enables the release of a secret at a future point in time. Many approaches implement Time-Locks as cryptographic puzzles, binding the recovery of the secret to the solution of the puzzle. Since the time required to find the puzzle's solution may vary due to a multitude of factors, including the computational effort spent, these solutions may not suit all scenarios.To overcome this limitation, we propose I Told You Tomorrow (ITYT), a novel way of implementing time-locked secrets based on smart contracts. ITYT relies on the blockchain to measure the elapse of time, and it combines threshold cryptography with economic incentives and penalties to replace cryptographic puzzles.We implement a prototype of ITYT on top of the Ethereum blockchain. The prototype leverages secure Multi-Party Computation to avoid any single point of trust. We also analyze resiliency to attacks with the help of economic game theory, in the context of rational adversaries. The experiments demonstrate the low cost and limited resource consumption associated with our approach.
CCS CONCEPTS• Security and privacy → Security services.
k-Anonymity and -diversity are two well-known privacy metrics that guarantee protection of the respondents of a dataset by obfuscating information that can disclose their identities and sensitive information. Existing solutions for enforcing them implicitly assume to operate in a centralized scenario, since they require complete visibility over the dataset to be anonymized, and can therefore have limited applicability in anonymizing large datasets. In this paper, we propose a solution that extends Mondrian (an efficient and effective approach designed for achieving k-anonymity) for enforcing both k-anonymity and -diversity over large datasets in a distributed manner, leveraging the parallel computation of multiple workers. Our approach efficiently distributes the computation among the workers, without requiring visibility over the dataset in its entirety. Our data partitioning limits the need for workers to exchange data, so that each worker can independently anonymize a portion of the dataset. We implemented our approach providing parallel execution on a dynamically chosen number of workers. The experimental evaluation shows that our solution provides scalability, while not affecting the quality of the resulting anonymization.
We present an approach for enabling a distributed anonymization process over large collections of sensor data. Our approach anonymizes large datasets (which might not fit in main memory) using an arbitrary number of workers within the Spark framework. We describe how to parallelize the anonymization process through a proper partitioning of the dataset. Our experimental evaluation shows that the proposed approach is scalable and do not affect the quality of the anonymized dataset.
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