Authentication of Moving Top-k Spatial Keyword Queries

published_or_final_versio

Query Authentication and processing on outsourced databases

Master'sMASTER OF SCIENC

Parity-based Data Outsourcing: Extension, Implementation, and Evaluation

Our research has developed a Parity-based Data Outsourcing (PDO) model. This model outsources a set of raw data by associating it with a set of parity data and then distributing both sets of data among a number of cloud servers that are managed independently by different service providers. Users query the servers for the data of their interest and are allowed to perform both authentication and correction. The former refers to the capability of verifying if the query result they receive is correct (i.e., all data items that satisfy the query condition are received, and every data item received is original from the data owner), whereas the latter, the capability of correcting the corrupted data, if any. Existing techniques all rely on complex cryptographic techniques and require the cloud server to build verification objects. In particular, they support only query authentication, but not error correction. In contrast, our approach enables users to perform both query authentication and error correction, and does so without having to install any additional software on a cloud server, which makes it possible to take advantage of the many cloud data management services available on the market today. This thesis makes the following contributions. 1) We extend the PDO model, which was originally designed for one-dimensional data, to handle multi-dimensional data. 2) We implement the PDO model, including parity coding, data encoding, data retrieval, query authentication and correction. 3) We evaluate the performance of the PDO model. We compare it with Merkle Hash Tree (MH-tree) and Signature Chain, two existing techniques that support query authentication, in terms of storage, communication, and computation overhead

AUTHENTICATION OF K NEAREST NEIGHBOR QUERY ON ROAD NETWORKS

ABSTRACT This work specifically focus on the k-nearest-neighbor (kNN) query verification on road networks and design verification schemes which support both distance verification and path verification. That is the k resulting objects have the shortest distances to the query point among all the objects in the database, and the path from the query point to each knearest-neighbor result is the valid shortest path on the network. In order to verify the kNN query result on a road network, a naïve solution would be to return the whole road network and the point of interest (POI) dataset to the client to show correctness and completeness of the result

Security and Privacy for Big Data: A Systematic Literature Review

Big data is currently a hot research topic, with four million hits on Google scholar in October 2016. One reason for the popularity of big data research is the knowledge that can be extracted from analyzing these large data sets. However, data can contain sensitive information, and data must therefore be sufficiently protected as it is stored and processed. Furthermore, it might also be required to provide meaningful, proven, privacy guarantees if the data can be linked to individuals. To the best of our knowledge, there exists no systematic overview of the overlap between big data and the area of security and privacy. Consequently, this review aims to explore security and privacy research within big data, by outlining and providing structure to what research currently exists. Moreover, we investigate which papers connect security and privacy with big data, and which categories these papers cover. Ultimately, is security and privacy research for big data different from the rest of the research within the security and privacy domain? To answer these questions, we perform a systematic literature review (SLR), where we collect recent papers from top conferences, and categorize them in order to provide an overview of the security and privacy topics present within the context of big data. Within each category we also present a qualitative analysis of papers representative for that specific area. Furthermore, we explore and visualize the relationship between the categories. Thus, the objective of this review is to provide a snapshot of the current state of security and privacy research for big data, and to discover where further research is required

Free-text Keystroke Authentication using Transformers: A Comparative Study of Architectures and Loss Functions

Keystroke biometrics is a promising approach for user identification and verification, leveraging the unique patterns in individuals' typing behavior. In this paper, we propose a Transformer-based network that employs self-attention to extract informative features from keystroke sequences, surpassing the performance of traditional Recurrent Neural Networks. We explore two distinct architectures, namely bi-encoder and cross-encoder, and compare their effectiveness in keystroke authentication. Furthermore, we investigate different loss functions, including triplet, batch-all triplet, and WDCL loss, along with various distance metrics such as Euclidean, Manhattan, and cosine distances. These experiments allow us to optimize the training process and enhance the performance of our model. To evaluate our proposed model, we employ the Aalto desktop keystroke dataset. The results demonstrate that the bi-encoder architecture with batch-all triplet loss and cosine distance achieves the best performance, yielding an exceptional Equal Error Rate of 0.0186%. Furthermore, alternative algorithms for calculating similarity scores are explored to enhance accuracy. Notably, the utilization of a one-class Support Vector Machine reduces the Equal Error Rate to an impressive 0.0163%. The outcomes of this study indicate that our model surpasses the previous state-of-the-art in free-text keystroke authentication. These findings contribute to advancing the field of keystroke authentication and offer practical implications for secure user verification systems

Query Racing: Fast Completeness Certification of Query Results

International audienceWe present a general and effective method to certify completeness of query results on relational tables stored in an untrusted DBMS. Our main contribution is the concept of "Query Race": we split up a general query into several single attribute queries, and exploit concurrency and speed to bind the complexity to the fastest of them. Our method supports selection queries with general composition of conjunctive and disjunctive order-based conditions on different attributes at the same time. To achieve our results, we require neither previous knowledge of queries nor specific support by the DBMS. We validate our approach with experimental results performed on a prototypical implementation