A novel approach to survivor memory unit of Decision Feedback Sequence Estimator(DFSE) for 1000BASE-T transceiver based on hybrid architecture of the classical register-exchange and trace-back methods is proposed.The ...A novel approach to survivor memory unit of Decision Feedback Sequence Estimator(DFSE) for 1000BASE-T transceiver based on hybrid architecture of the classical register-exchange and trace-back methods is proposed.The proposed architecture is investigated with special emphasis on low power and small decoder latency,in which a dedicated register-exchange module is designed to provide tentative survivor symbols with zero latency,and a high-speed trace back logic is presented to meet the tight latency budget specified for 1000BASE-T transceiver.Furthermore,clock-gating register banks are constructed for power saving.VLSI implementation reveals that,the proposed architecture provides about 40% savings in power consumption compared to the traditional register-exchange architecture.展开更多
The widespread use of Location-Based Services （LBSs）, which allows untrusted service providers to collect large quantities of information regarding users＇ locations, has raised serious privacy concerns. In response...The widespread use of Location-Based Services （LBSs）, which allows untrusted service providers to collect large quantities of information regarding users＇ locations, has raised serious privacy concerns. In response to these issues, a variety of LBS Privacy Protection Mechanisms （LPPMs） have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary＇s estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.展开更多
文摘A novel approach to survivor memory unit of Decision Feedback Sequence Estimator(DFSE) for 1000BASE-T transceiver based on hybrid architecture of the classical register-exchange and trace-back methods is proposed.The proposed architecture is investigated with special emphasis on low power and small decoder latency,in which a dedicated register-exchange module is designed to provide tentative survivor symbols with zero latency,and a high-speed trace back logic is presented to meet the tight latency budget specified for 1000BASE-T transceiver.Furthermore,clock-gating register banks are constructed for power saving.VLSI implementation reveals that,the proposed architecture provides about 40% savings in power consumption compared to the traditional register-exchange architecture.
基金supported in part by the National Science and Technology Major Project (No. 2012ZX03002001004)the National Natural Science Foundation of China (Nos. 61172090, 61163009, and 61163010)+1 种基金the PhD Programs Foundation of Ministry of Education of China (No. 20120201110013)the Scientific and Technological Project in Shaanxi Province (Nos. 2012K06-30 and 2014JQ8322)
文摘The widespread use of Location-Based Services （LBSs）, which allows untrusted service providers to collect large quantities of information regarding users＇ locations, has raised serious privacy concerns. In response to these issues, a variety of LBS Privacy Protection Mechanisms （LPPMs） have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary＇s estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.
