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The rapid expansion of location-based services gives rise to significant security and privacy apprehensions. While these services deliver convenience, they accentuate concerns regarding widespread location tracking via web services, mobile apps, IoT devices, and autonomous vehicles. In this study, we comprehensively assess the merits and constraints of prevalent techniques in location privacy protection, including spatial-temporal cloaking, k-anonymity, differential privacy, and encryption. Furthermore, we delve into emerging applications like intelligent traffic planning and virus contact tracing which introduce novel complexities to the pursuit of robust location privacy safeguards. 

Abstract Contrary to topological insulators, topological semimetals possess a nontrivial chiral anomaly that leads to negative magnetoresistance and are hosts to both conductive bulk states and topological surface states with intriguing transport properties for spintronics. Here, we fabricate highly-ordered metallic Pt₃Sn and Pt₃Sn_xFe_1-xthin films via sputtering technology. Systematic angular dependence (both in-plane and out-of-plane) study of magnetoresistance presents surprisingly robust quadratic and linear negative longitudinal magnetoresistance features for Pt₃Sn and Pt₃Sn_xFe_1-x, respectively. We attribute the anomalous negative longitudinal magnetoresistance to the type-II Dirac semimetal phase (pristine Pt₃Sn) and/or the formation of tunable Weyl semimetal phases through symmetry breaking processes, such as magnetic-atom doping, as confirmed by first-principles calculations. Furthermore, Pt₃Sn and Pt₃Sn_xFe_1-xshow the promising performance for facilitating the development of advanced spin-orbit torque devices. These results extend our understanding of chiral anomaly of topological semimetals and can pave the way for exploring novel topological materials for spintronic devices. 

Background While genomic variations can provide valuable information for health care and ancestry, the privacy of individual genomic data must be protected. Thus, a secure environment is desirable for a human DNA database such that the total data are queryable but not directly accessible to involved parties (eg, data hosts and hospitals) and that the query results are learned only by the user or authorized party. Objective In this study, we provide efficient and secure computations on panels of single nucleotide polymorphisms (SNPs) from genomic sequences as computed under the following set operations: union, intersection, set difference, and symmetric difference. Methods Using these operations, we can compute similarity metrics, such as the Jaccard similarity, which could allow querying a DNA database to find the same person and genetic relatives securely. We analyzed various security paradigms and show metrics for the protocols under several security assumptions, such as semihonest, malicious with honest majority, and malicious with a malicious majority. Results We show that our methods can be used practically on realistically sized data. Specifically, we can compute the Jaccard similarity of two genomes when considering sets of SNPs, each with 400,000 SNPs, in 2.16 seconds with the assumption of a malicious adversary in an honest majority and 0.36 seconds under a semihonest model. Conclusions Our methods may help adopt trusted environments for hosting individual genomic data with end-to-end data security.