14,048 research outputs found
CYCLOSA: Decentralizing Private Web Search Through SGX-Based Browser Extensions
By regularly querying Web search engines, users (unconsciously) disclose
large amounts of their personal data as part of their search queries, among
which some might reveal sensitive information (e.g. health issues, sexual,
political or religious preferences). Several solutions exist to allow users
querying search engines while improving privacy protection. However, these
solutions suffer from a number of limitations: some are subject to user
re-identification attacks, while others lack scalability or are unable to
provide accurate results. This paper presents CYCLOSA, a secure, scalable and
accurate private Web search solution. CYCLOSA improves security by relying on
trusted execution environments (TEEs) as provided by Intel SGX. Further,
CYCLOSA proposes a novel adaptive privacy protection solution that reduces the
risk of user re- identification. CYCLOSA sends fake queries to the search
engine and dynamically adapts their count according to the sensitivity of the
user query. In addition, CYCLOSA meets scalability as it is fully
decentralized, spreading the load for distributing fake queries among other
nodes. Finally, CYCLOSA achieves accuracy of Web search as it handles the real
query and the fake queries separately, in contrast to other existing solutions
that mix fake and real query results
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Two-fold Semantic Web service matchmaking – applying ontology mapping for service discovery
Semantic Web Services (SWS) aim at the automated discovery and orchestration of Web services on the basis of comprehensive, machine-interpretable semantic descriptions. Since SWS annotations usually are created by distinct SWS providers, semantic-level mediation, i.e. mediation between concurrent semantic representations, is a key requirement for SWS discovery. Since semantic-level mediation aims at enabling interoperability across heterogeneous semantic representations, it can be perceived as a particular instantiation of the ontology mapping problem. While recent SWS matchmakers usually rely on manual alignments or subscription to a common ontology, we propose a two-fold SWS matchmaking approach, consisting of (a) a general-purpose semantic-level mediator and (b) comparison and matchmaking of SWS capabilities. Our semantic-level mediation approach enables the implicit representation of similarities across distinct SWS by grounding service descriptions in so-called Mediation Spaces (MS). Given a set of SWS and their respective grounding, a SWS matchmaker automatically computes instance similarities across distinct SWS ontologies and matches the request to the most suitable SWS. A prototypical application illustrates our approach
Improving the Representation and Conversion of Mathematical Formulae by Considering their Textual Context
Mathematical formulae represent complex semantic information in a concise
form. Especially in Science, Technology, Engineering, and Mathematics,
mathematical formulae are crucial to communicate information, e.g., in
scientific papers, and to perform computations using computer algebra systems.
Enabling computers to access the information encoded in mathematical formulae
requires machine-readable formats that can represent both the presentation and
content, i.e., the semantics, of formulae. Exchanging such information between
systems additionally requires conversion methods for mathematical
representation formats. We analyze how the semantic enrichment of formulae
improves the format conversion process and show that considering the textual
context of formulae reduces the error rate of such conversions. Our main
contributions are: (1) providing an openly available benchmark dataset for the
mathematical format conversion task consisting of a newly created test
collection, an extensive, manually curated gold standard and task-specific
evaluation metrics; (2) performing a quantitative evaluation of
state-of-the-art tools for mathematical format conversions; (3) presenting a
new approach that considers the textual context of formulae to reduce the error
rate for mathematical format conversions. Our benchmark dataset facilitates
future research on mathematical format conversions as well as research on many
problems in mathematical information retrieval. Because we annotated and linked
all components of formulae, e.g., identifiers, operators and other entities, to
Wikidata entries, the gold standard can, for instance, be used to train methods
for formula concept discovery and recognition. Such methods can then be applied
to improve mathematical information retrieval systems, e.g., for semantic
formula search, recommendation of mathematical content, or detection of
mathematical plagiarism.Comment: 10 pages, 4 figure
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