Online optimization and fair costing for dynamic data sharing in a cloud data market

Abstract

The data markets are emerging to address many organizations' need to find more useful external data sets for deeper insights. Existing data markets have two main limitations. First, they either sell a whole data set or some fixed views, but do not allow arbitrary adhoc queries. Second, they only sell static data, but not data that are frequently updated. While there exist proposed solutions for selling ad-hoc queries, it is an open question what mechanism should be used to sell ad-hoc queries on dynamic data. In this paper, we study a data market framework that enables the sale or sharing of dynamic data, where each sharing is specified by an ad-hoc query. To keep the shared data up-to-date, the service provider should create a view of the shared data and maintain the view for the data buyer. We provide solutions to two important problems in this framework: how to efficiently maintain the views, and how to fairly determine the cost each sharing incurs for its view to be created and maintained by the service provider. Both problems are challenging since in the first problem, different sharings with the same operations in their plans may reuse these operations, and each sharing plan must be generated online. In the second problem, there are several factors that impact the fairness of a costing function and a straightforward mechanism won't work. We propose an intuitive online algorithm for sharing plan selection, as well as a set of fair costing criteria and an algorithm that maximizes the fairness.

A Self-Controllable and Balanced Data Sharing Model

“…Whereas the PEP is implemented by smart contract, which is written in Solidity, 6 a programming language for Ethereum or Quorum. The interactions between smart contract and FACPL components are implemented by web3j 7…”

“…So far, the proposals for advanced data sharing mainly focus on query-related, such as privacy-preserving, pricing private data [11], dynamic data sharing or ad-hoc query [7], query combination and optimization [35], fair trading [36], access control [34]. In their setting, C asks a request and pays for them; the market maker answers C and compensates O, as the query may leak O's privacy.…”

“…In terms of pricing and compensation, various data price models have been proposed in the data sharing system, such as facilitating data market price model [10], query-based data price model [40], history-aware price model [41], Internet-based views price model [42], randomsampling-based price model [43] and the generic data pricing model [44]. Our price compensation is based on Li and Hacigümüs [7], which assigns price to noisy query answers as a function of requestor's accuracy. Furthermore, we propose the personalized price mechanism (33).…”

“…) is a valid request, according to(8),(7) and(19), it should have,∃acr, acr ∈ acp ∧ acr.i ⊆ ar.i ∧ ar.y ⊆ acr.o (22) 3) MATCHING PRIVACY PREFERENCESThird, the valid request should satisfy privacy preferences. Both O and C have their own privacy preferences that should be considered in our data sharing model.…”

Cloud Based Mobile Network Sharing: A New Model

“…If any function is overloaded, then cloud manager immediately deploys the new virtual machine and moves the active users to newly deployed function. In this case, the active users' context will transfer from old VM to newly deployed VM without interrupting the active sessions (similar to [34]). The VM deployment can be at same location or also another location.…”