Then, to thwart the threats from those LBS applications, we exploit the deceptive dummy techniques and design a dummy-based location privacy-preserving scheme, named DLP, which comprises three algorithms, namely, Spread, Shift, and Switch. Aiming at the challenge, in this paper, we first build up a Detect Module (DM) and employ it to investigate more than 80% of LBS applications are keen on tracking users. Nevertheless, since those LBS applications will continuously collect and disclose users’ location data, major concerns on privacy leakage are raised. In addition, our privacy protection provides a trade-off between privacy (i.e., avoid revealing its true location) and utility (i.e., still benefiting from services such as places recommendation) fully controllable by the users.Īs a straightforward consequence of advances in the Internet of Things (IoT), location-based service (LBS) applications have been pervasive in our daily lives. Consequently, the capacity of positioning systems to extract points of interest of users from received requests is highly limited (a decrease of 50% on average). Indeed, by leveraging a caching strategy, requests are only sent when users visit new areas. We show that the proposed approach drastically reduces the exposure of the user's location to positioning systems (up to 95%). We exhaustively evaluate our solution with a real dataset of mobility traces. The key idea behind our online approach is to combine a caching strategy (for limiting the exposure of the user's position for already visited locations) and a random sampling (for controlling the precision of revealed information). In this paper, we propose a novel solution to preserve users’ privacy when requesting users’ location from Wi-Fi while supporting high utility. By doing so, mobile users are revealing their mobility to the location provider, potentially exposing sensitive information to an untrusted third-party. To enable this network-based geolocation, mobile devices need to interact with a location positioning system that will resolve a list of visible Wi-Fi access points into a position. Despite a lower accuracy, Wi-Fi based geolocation has several advantages over GPS such as reduced energy consumption and availability in indoor and availability in indoor environments. On mobile devices, the geolocation can be obtained via GPS or by leveraging surrounding network infrastructure such as Wi-Fi access points. With the democratization of mobile devices embedding different positioning capabilities, location information is used for a variety of applications. Our extensive experiments demonstrate that the proposed approach could improve user experience up to >50% compared with the benchmark algorithms. It not only merges the overlapping data in the cache to save space but also places data into multi-level caching devices driven by user experience. To address the above problems, a cache management approach adapting quantitative trading platform is proposed. (2) This platform uses a wide variety of caching devices with heterogeneous performance. (1) Data access of users has overlapping characteristics for time-series data. However, quantitative trading platform has its special demands.
Research work on cache management has achieved many referential results. In the aspect of optimising data access time, cache management is a critical link. In using the platform, return time of backtesting historical data is a key factor that influences user experience. With the rapid development of quantitative trading business in the field of investment, quantitative trading platform is becoming an important tool for numerous investing users to participate in quantitative trading.
0 kommentar(er)
