Searchable symmetric encryption (SSE) enables a client to perform searches over its outsourced encrypted files while preserving privacy of the files and queries. Dynamic schemes, where files can be added or removed, leak more information than static schemes. For dynamic schemes, forward privacy requires that a newly added file cannot be linked to previous searches. We present a new dynamic SSE scheme that achieves forward privacy by replacing the keys revealed to the server on each search. Our scheme is efficient and parallelizable and outperforms the best previous schemes providing forward privacy, and achieves competitive performance with dynamic schemes without forward privacy. We provide a full security proof in the random oracle model. In our experiments on the Wikipedia archive of about four million pages, the server takes one second to perform a search with 100,000 results.
With the growing trend toward using outsourced storage, the problem of efficiently checking and proving data integrity needs more consideration. Starting with PDP and POR schemes in 2007, many cryptography and security researchers have addressed the problem. After the first solutions for static data, dynamic versions were developed (e.g., DPDP). Researchers also considered distributed versions of such schemes. Alas, in all such distributed schemes, the client needs to be aware of the structure of the cloud, and possibly pre-process the file accordingly, even though the security guarantees in the real world are not improved.We propose a distributed and replicated DPDP which is transparent from the client's viewpoint. It allows for real scenarios where the cloud storage provider (CSP) may hide its internal structure from the client, flexibly manage its resources, while still providing provable service to the client. The CSP decides on how many and which servers will store the data. Since the load is distributed on multiple servers, we observe one-to-two orders of magnitude better performance in our tests, while availability and reliability are also improved via replication. In addition, we use persistent rank-based authenticated skip lists to create centralized and distributed variants of a dynamic version control system with optimal complexity.
Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict transportation modes. This framework follows a sequence of five steps: (i) data preparation, where GPS points are grouped in trajectory samples; (ii) point features generation; (iii) trajectory features extraction; (iv) noise removal; (v) normalization. We show that the extraction of the new point features: bearing rate, the rate of rate of change of the bearing rate and the global and local trajectory features, like medians and percentiles enables many classifiers to achieve high accuracy (96.5%) and f1 (96.3%) scores. We also show that the noise removal task affects the performance of all the models tested. Finally, the empirical tests where we compare this work against state-of-art transportation mode prediction strategies show that our framework is competitive and outperforms most of them.
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