The evaluation of ecosystem services can provide essential help in incorporatating the multifunctionality of urban ecosystems in planning and management processes. Two important regulating services of urban trees, carbon sequestration and air pollution removal, are evaluated in this article for different types of tree stands (streets, parks) in the city centre of Szeged (Hungary). The necessary calculations were carried out by an adaptation of the targeted model (i-Tree Eco), based on a large complete tree inventory dataset. The analyses revealed the main tendencies in differences between tree species considering the tree condition, which affects the service-providing capacity to a high degree. The effects of differences in tree management on the chosen ecosystem services were investigated by comparing two pairs of tree alleys. Based on our observations, clear cuts and complete tree alley changes are not advisable from an ecosystem service point of view.
In urban areas vegetation (especially woody vegetation) is of utmost importance, since it affects the ecological conditions of the city. Urban trees play an important role in improving urban climate both at the local (city, district) and the micro-level (e.g. in public squares). Establishing and maintaining advanced and detailed information systems necessary for the management of urban tree stands is an important task of environmental and climate-conscious city management. Despite that, few of the Hungarian municipalities have a regularly updated tree database. The city of Szeged started efficient green space management in autumn 2013, when we started the creation of a detailed and up-to-date tree register for the public areas, which has been continuously expanded ever since. The survey of the present study covers the period of the growing season, from late spring to early autumn of 2013. All the trees are included in the survey and quite a number of data are recorded for each individual (e.g. species, age, size parameters, exact location, health status, etc.). The recorded data are paper-based, however they are included in a GIS-based green space inventory software, Greenformatic, where coordinates are associated to each object, while information on the state of the tree, its location and handling can be found in the attribute table. The trees included are mostly concentrated in the inner city of Szeged, but the surveys will gradually cover ever larger areas of the city. The results highlight the fact that the structural attributes of the different species’ populations are formed by the integrated effect of the species’ urban tolerance and planting policies of the past decades. The current database already allows highly complex analysis, which contributes to the well-being of city residents.
Private set intersection (PSI) is a cryptographic technique that is applicable to many privacy-sensitive scenarios. For decades, researchers have been focusing on improving its efficiency in both communication and computation. However, most of the existing solutions are inefficient for an unequal number of inputs, which is common in conventional client-server settings. In this paper, we analyze and optimize the efficiency of existing PSI protocols to support precomputation so that they can efficiently deal with such input sets. We transform four existing PSI protocols into the precomputation form such that in the setup phase the communication is linear only in the size of the larger input set, while in the online phase the communication is linear in the size of the smaller input set. We implement all four protocols and run experiments between two PCs and between a PC and a smartphone and give a systematic comparison of their performance. Our experiments show that a protocol based on securely evaluating a garbled AES circuit achieves the fastest setup time by several orders of magnitudes, and the fastest online time in the PC setting where AES-NI acceleration is available. In the mobile setting, the fastest online time is achieved by a protocol based on the Diffie-Hellman assumption.
Decision trees and random forests are widely used classifiers in machine learning. Service providers often host classification models in a cloud service and provide an interface for clients to use the model remotely. While the model is sensitive information of the server, the input query and prediction results are sensitive information of the client. This motivates the need for private decision tree evaluation, where the service provider does not learn the client’s input and the client does not learn the model except for its size and the result.
In this work, we identify the three phases of private decision tree evaluation protocols: feature selection, comparison, and path evaluation. We systematize constant-round protocols for each of these phases to identify the best available instantiations using the two main paradigms for secure computation: garbling techniques and homomorphic encryption. There is a natural tradeoff between runtime and communication considering these two paradigms: garbling techniques use fast symmetric-key operations but require a large amount of communication, while homomorphic encryption is computationally heavy but requires little communication. Our contributions are as follows: Firstly, we systematically review and analyse state-of-the-art protocols for the three phases of private decision tree evaluation. Our methodology allows us to identify novel combinations of these protocols that provide better tradeoffs than existing protocols. Thereafter, we empirically evaluate all combinations of these protocols by providing communication and runtime measures, and provide recommendations based on the identified concrete tradeoffs.