Smart spaces, or smart environments, represent the next evolutionary development in buildings, banking, homes, hospitals, transportation systems, industries, cities, and government automation. By riding the tide of sensor and event processing technologies, the smart environment captures and processes information about its surroundings as well as its internal settings to support context awareness and intelligent inference. For example, in long term home health monitoring, automatic detection of activity and health status allows elders to receive continuous care at home, thus reducing health care costs, improving quality of life, and enabling independence. Moreover, one promising goal of smart spaces, "collecting and accessing information everywhere anytime", triggers the needs for efficient yet secured information sharing among smart spaces. In this dissertation, we explore new approaches to address challenges on processing high-level events and securing information sharing in the context of semantic spaces. We first explore the event processing solutions in smart spaces. We propose an event processing framework, which includes two sub-processes: stream event processing and semantic event processing. Stream event processing extracts knowledge from sensor streams for each modality. We present a novel model to recognize motions of the objects attached with passive RFID tags. The model applies Hidden Markov Model (HMM) to accurately infer the motion sequence based on the relative variance in a time series of response rates. The model is augmented with an adaptive model, which can be used to dynamically adjust to the changing environment based on the change-point detection algorithm. Moreover, an online Viterbi algorithm is developed to get low output latency. Then, we turn our attention to inferring high-level semantic events based on the preliminary semantic events from stream event processing. We propose event ontology to enable semantic indexing and detecting of machine-processable events and exchanging event data among heterogeneous engines. Moreover, we develop an event processing framework driven by the event ontology, OntoCEP, to elaborate event composition and semantic reasoning, which apply event patterns and context reasoning rules into one coherent system. The event composition engine conducts detection of complex patterns of events based on the relationships such as causality, and timing relationships. The semantic reasoning engine integrates context knowledge from which more meaningful events are deduced. Finally, we investigate how to secure sharing of complex data objects among pervasive information systems. To address the challenges posed by heterogeneous data sources, complex objects and context dynamics, we propose an advanced authorization model that supports specifying and enforcing authorizations in flexible and efficient ways. The model employs semantic web technologies to conceptualize data and explicitly express the relationships among concepts and instances involved in information sharing. Authorizations can be specified at different levels of the predefined concept hierarchies and be propagated to lower-levels. A novel decision propagation model is proposed to enable fast evaluation and updating of concept-level access decisions. To resolve conflicts among policies, we model a policy set as a semilattice, upon which a binary operation is defined to adapt to various requirements. Moreover, enabled by ontology reasoning tools, a flexible specification approach of authorization, namely rule-based policy generation, is developed to encode context dynamics, making the authorization enforcement adaptive to contexts. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]