Search geometric ranges efficiently as keywords over encrypted spatial data

With the increasing popularity of location-based services(LBS),data outsourcing toward clouds is an emerging paradigm for ease of data management by LBS providers.Geometric range queries are one of the fundamental search functions in LBS,which are to find points inside geometric areas(e.g.,circles or polygons).To ensure data confidentiality,the service users tend to encrypt the data before outsourcing it.However,regarding encrypted data,only a few consider geometric range queries,where the rationale is the high-dimension calculations make these queries particularly harder.In this paper,we propose a novel scheme for geometric range queries,that can provide the privacy of data stored at a cloud server and queries.Our scheme supports querying encrypted spatial data with irregular-shaped areas,achieves fast searches and enables dynamic updates.Experimental results over real-world spatial datasets demonstrate that our scheme results in fewer communication rounds and can speed up the search time 4×compared to state-of-the-art schemes,without carrying any potentially visible leakage in the structure.

DBT-PDP:Provable data possession with outsourced data batch transfer based on blockchain

In the scenario of large-scale data ownership transactions,existing data integrity auditing schemes are faced with security risks from malicious third-party auditors and are inefficient in both calculation and communication,which greatly affects their practicability.This paper proposes a data integrity audit scheme based on blockchain where data ownership can be traded in batches.A data tag structure which supports data ownership batch transaction is adopted in our scheme.The update process of data tag does not involve the unique information of each data,so that any user can complete ownership transactions of multiple data in a single transaction through a single transaction auxiliary information.At the same time,smart contract is introduced into our scheme to perform data integrity audit belongs to third-party auditors,therefore our scheme can free from potential security risks of malicious third-party auditors.Safety analysis shows that our scheme is proved to be safe under the stochastic prediction model and k-CEIDH hypothesis.Compared with similar schemes,the experiment shows that communication overhead and computing time of data ownership transaction in our scheme is lower.Meanwhile,the communication overhead and computing time of our scheme is similar to that of similar schemes in data integrity audit.