Brief announcement: asynchronous verifiable information dispersal with near-optimal communication

CNS-1718135 - National Science Foundation; CNS-1801564 - National Science Foundation; CNS-1931714 - National Science Foundation; CNS-1915763 - National Science Foundation; HR00112020021 - Department of Defense/DARPA; 000000000000000000000000000000000000000000000000000000037211 - SRI Internationalhttps://eprint.iacr.org/2022/775.pdfFirst author draf

A secure data outsourcing scheme based on Asmuth – Bloom secret sharing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Data outsourcing is an emerging paradigm for data management in which a database is provided as a service by third-party service providers. One of the major benefits of offering database as a service is to provide organisations, which are unable to purchase expensive hardware and software to host their databases, with efficient data storage accessible online at a cheap rate. Despite that, several issues of data confidentiality, integrity, availability and efficient indexing of users’ queries at the server side have to be addressed in the data outsourcing paradigm. Service providers have to guarantee that their clients’ data are secured against internal (insider) and external attacks. This paper briefly analyses the existing indexing schemes in data outsourcing and highlights their advantages and disadvantages. Then, this paper proposes a secure data outsourcing scheme based on Asmuth–Bloom secret sharing which tries to address the issues in data outsourcing such as data confidentiality, availability and order preservation for efficient indexing

Population Density-based Hospital Recommendation with Mobile LBS Big Data

The difficulty of getting medical treatment is one of major livelihood issues in China. Since patients lack prior knowledge about the spatial distribution and the capacity of hospitals, some hospitals have abnormally high or sporadic population densities. This paper presents a new model for estimating the spatiotemporal population density in each hospital based on location-based service (LBS) big data, which would be beneficial to guiding and dispersing outpatients. To improve the estimation accuracy, several approaches are proposed to denoise the LBS data and classify people by detecting their various behaviors. In addition, a long short-term memory (LSTM) based deep learning is presented to predict the trend of population density. By using Baidu large-scale LBS logs database, we apply the proposed model to 113 hospitals in Beijing, P. R. China, and constructed an online hospital recommendation system which can provide users with a hospital rank list basing the real-time population density information and the hospitals' basic information such as hospitals' levels and their distances. We also mine several interesting patterns from these LBS logs by using our proposed system

Asynchronous Verifiable Information Dispersal with Near-Optimal Communication

We present a near-optimal asynchronous verifiable information dispersal (AVID) protocol. The total dispersal cost of our AVID protocol is $O(|M|+\kappa n^2)$, and the retrieval cost per client is $O(|M|+\kappa n)$. Unlike prior works, our AVID protocol only assumes the existence of collision-resistant hash functions. Also, in our AVID protocol, the dispersing client incurs a communication cost of $O(|M|+\kappa n)$ in comparison to $O(|M|+\kappa n\log n)$ of prior best. Moreover, each node in our AVID protocol incurs a storage cost of $O(|M|/n+\kappa)$ bits, in comparison to $O(|M|/n+\kappa \log n)$ bits of prior best. Finally, we present lower bound results on communication cost and show that our AVID protocol has near-optimal communication costs -- only a factor of $O(\kappa)$ gap from the lower bounds

File management in a mobile DHT-based P2P environment

The emergence of mobile P2P systems is largely due to the evolution of mobile devices into powerful information processing units. The relatively structured context that results from the mapping of mobile patterns of behaviour onto P2P models is however constrained by the vulnerabilities of P2P networks and the inherent limitations of mobile devices. Whilst the implementation of P2P models gives rise to security and reliability issues, the deployment of mobile devices is subject to efficiency constraints. This paper presents the development and deployment of a mobile P2P system based on distributed hash tables (DHT). The secure, reliable and efficient dispersal of files is taken as an application. Reliability was addressed by providing two methods for file dispersal: replication and erasure coding. Security constraints were catered for by incorporating an authentication mechanism and three encryption schemes. Lightweight versions of various algorithms were selected in order to attend to efficiency requirements