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In location-based services, users with location-aware mobile devices are able to make queries about their surroundings anywhere and at any time. While this ubiquitous computing paradigm brings great convenience for information access, it also raises concerns over potential intrusion into user location privacy. To protect location privacy, one typical approach is to cloak user locations into spatial regions based on user-specified privacy requirements, and to transform location-based queries into region-based queries. In this paper, we identify and address three new issues concerning this location cloaking approach. First, we study the representation of cloaking regions and show that a circular region generally leads to a small result size for region-based queries. Second, we develop a mobility-aware location cloaking technique to resist trace analysis attacks. Two cloaking algorithms, namely MaxAccu_Cloak and MinComm_Cloak, are designed based on different performance objectives. Finally, we develop an efficient polynomial algorithm for evaluating circular-region-based kNN queries. Two query processing modes, namely bulk and progressive, are presented to return query results either all at once or in an incremental manner. Experimental results show that our proposed mobility-aware cloaking algorithms significantly improve the quality of location cloaking in terms of an entropy measure without compromising much on query latency or communication cost. Moreover, the progressive query processing mode achieves a shorter response time than the bulk mode by parallelizing the query evaluation and result transmission.

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The fulfillment of individual customer affective needs may award the producer extra premium in gaining a competitive edge. This entails a number of technical challenges to be addressed, such as the elicitation, evaluation, and fulfillment of affective needs, as well as the evaluation of affordability of producers to launch the planned products. Mass customization and personalization have been recognized as an effective means to enhance front-end customer satisfaction while maintaining backend production efficiency. This paper proposes an affective design framework to facilitate decision-making in designing customized product ecosystems. In particular, ambient intelligence techniques are applied to elicit affective customer needs. An analytical model is proposed to support affective design analysis. Utility measure and conjoint analysis are employed to quantify affective satisfaction, while the producer affordability is evaluated using an affordability index. Association rule mining techniques are applied to model the mapping of affective needs to design elements. Configuration design of product ecosystems is optimized with a heuristic genetic algorithm. A case study of Volvo truck cab design is reported with a focus on the customization of affective features. It is demonstrated that the analytical affective design framework can effectively manage the elicitation, analysis, and fulfillment of affective customer needs. Meanwhile, it can account for the manufacturer's capabilities, which is vital for ensuring a profit margin in the mass customization and personalization endeavor.