Improving Efficiency, Expressiveness and Security of Searchable Encryption

A large part of our personal data, ranging from medical and financial records to our social activity, is stored online in cloud servers. Frequent data breaches threaten to expose these data to malicious third parties, often with catastrophic consequences (estimated to several billion of US dollars annually). In this thesis, we use, extend and improve Searchable Encryption (SE) in order to build the next generation encrypted databases/systems that will prevent such undesirable situations. Our goal is to build systems that are both practical and provably secure, while allowing expressive search and computation on encrypted data. Towards this goal, we have proposed new SE schemes that achieve the following: (i) have better search/computation time, (ii) allow expressive queries such as range, join, group-by, as well as dynamic query workloads, and (iii) provide new adjustable security-efficiency trade-offs---leading to robust and efficient schemes even against very powerful adversaries

Populism, Ethnic Nationalism and Xenophobia

In this paper, we study a set of new indices, which are based on the answers of citizens to certain batteries of items included in a CSES module 5 pilot study conducted in Greece after the parliamentary election of September 2015. The first index is used to capture attitudes of citizens towards the political elites and is related to the increasing number of recent publications focusing on the study of populist attitudes. Likewise, the second index is based on items related to a demand for more power to the poor people. Another index developed here is built to measure attitudes towards out-groups. The use of this index is motivated by the increasing power of radical right-wing anti-immigrant parties, especially in Europe and due, to a certain extent, to the recent immigrant crisis. In addition to the aforementioned indices, we also identify the characteristics respondents think to be the most important for someone to be considered as a "Real Greek", i.e. we present what are the most important lines that according to Greek citizens separate the in-group from the out-groups

Populism, Ethnic Nationalism and Xenophobia

In this paper, we study a set of new indices, which are based on the answers of citizens to certain batteries of items included in a CSES module 5 pilot study conducted in Greece after the parliamentary election of September 2015. The first index is used to capture attitudes of citizens towards the political elites and is related to the increasing number of recent publications focusing on the study of populist attitudes. Likewise, the second index is based on items related to a demand for more power to the poor people. Another index developed here is built to measure attitudes towards out-groups. The use of this index is motivated by the increasing power of radical right-wing anti-immigrant parties, especially in Europe and due, to a certain extent, to the recent immigrant crisis. In addition to the aforementioned indices, we also identify the characteristics respondents think to be the most important for someone to be considered as a "Real Greek", i.e. we present what are the most important lines that according to Greek citizens separate the in-group from the out-groups

Detection of somatostatin receptors in human osteosarcoma

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Dynamic Searchable Encryption with Optimal Search in the Presence of Deletions

We focus on the problem of Dynamic Searchable Encryption (DSE) with efficient (optimal/quasi-optimal) search in the presence of deletions. Towards that end, we first propose $\mathsf{OSSE}$, the first DSE scheme that can achieve asymptotically optimal search time, linear to the result size and independent of any prior deletions, improving the previous state of the art by a multiplicative logarithmic factor. We then propose our second scheme $\mathsf{LLSE}$, that achieves a sublogarithmic search overhead ($\log\log i_w$, where $i_w$ is the number or prior insertions for a keyword) compared to the optimal achieved by $\mathsf{OSSE}$. While this is slightly worse than our first scheme, it still outperforms prior works, while also achieving faster deletions and asymptotically smaller server storage. Both schemes have standard leakage profiles and are forward-and-backward private. Our experimental evaluation is very encouraging as it shows our schemes consistently outperform the prior state-of-the-art DSE by 1.2-6.6$\times$ in search computation time, while also requiring just a single roundtrip to receive the search result. Even compared with prior simpler and very efficient constructions in which all deleted records are returned as part of the result, our $\mathsf{OSSE}$ achieves better performance for deletion rates ranging from 45-55%, while the previous state-of-the-art quasi-optimal scheme achieves this for 65-75% deletion rates

Dynamic Searchable Encryption with Small Client Storage

We study the problem of dynamic searchable encryption (DSE) with forward-and-backward privacy. Many DSE schemes have been proposed recently but the most efficient ones have one limitation: they require maintaining an operation counter for each unique keyword, either stored locally at the client or accessed obliviously (e.g., with an oblivious map) at the server, during every operation. We propose three new schemes that overcome the above limitation and achieve constant permanent client storage with improved performance, both asymptotically and experimentally, compared to prior state-of-the-art works. In particular, our first two schemes adopt a “static-to-dynamic” transformation which eliminates the need for oblivious accesses during searches. Due to this, they are the first practical schemes with minimal client storage and non-interactive search. Our third scheme is the first quasi-optimal forward-and-backward DSE scheme with only a logarithmic overhead for retrieving the query result (independently of previous deletions). While it does require an oblivious access during search in order to keep permanent client storage minimal, its practical performance is up to four orders of magnitude better than the best existing scheme with quasi-optimal search

Snoopy: Surpassing the Scalability Bottleneck of Oblivious Storage

Existing oblivious storage systems provide strong security by hiding access patterns, but do not scale to sustain high throughput as they rely on a central point of coordination. To overcome this scalability bottleneck, we present Snoopy, an object store that is both oblivious and scalable such that adding more machines increases system throughput. Snoopy contributes techniques tailored to the high-throughput regime to securely distribute and efficiently parallelize every system component without prohibitive coordination costs. These techniques enable Snoopy to scale similarly to a plaintext storage system. Snoopy achieves 13.7x higher throughput than Obladi, a state-of-the-art oblivious storage system. Specifically, Obladi reaches a throughput of 6.7K requests/s for two million 160-byte objects and cannot scale beyond a proxy and server machine. For the same data size, Snoopy uses 18 machines to scale to 92K requests/s with average latency under 500ms

Practical Private Range Search in Depth

We consider a data owner that outsources its dataset to an untrusted server. The owner wishes to enable the server to answer range queries on a single attribute, without compromising the privacy of the data and the queries. There are several schemes on “practical” private range search (mainly in database venues) that attempt to strike a trade-off between efficiency and security. Nevertheless, these methods either lack provable security guarantees or permit unacceptable privacy leakages. In this article, we take an interdisciplinary approach, which combines the rigor of security formulations and proofs with efficient data management techniques. We construct a wide set of novel schemes with realistic security/performance trade-offs, adopting the notion of Searchable Symmetric Encryption (SSE), primarily proposed for keyword search. We reduce range search to multi-keyword search using range-covering techniques with tree-like indexes, and formalize the problem as Range Searchable Symmetric Encryption (RSSE). We demonstrate that, given any secure SSE scheme, the challenge boils down to (i) formulating leakages that arise from the index structure and (ii) minimizing false positives incurred by some schemes under heavy data skew. We also explain an important concept in the recent SSE bibliography, namely locality, and design generic and specialized ways to attribute locality to our RSSE schemes. Moreover, we are the first to devise secure schemes for answering range aggregate queries, such as range sums and range min/max. We analytically detail the superiority of our proposals over prior work and experimentally confirm their practicality

Structural Damage Prediction of a Reinforced Concrete Frame under Single and Multiple Seismic Events Using Machine Learning Algorithms

Advanced machine learning algorithms have the potential to be successfully applied to many areas of system modelling. In the present study, the capability of ten machine learning algorithms to predict the structural damage of an 8-storey reinforced concrete frame building subjected to single and successive ground motions is examined. From this point of view, the initial damage state of the structural system, as well as 16 well-known ground motion intensity measures, are adopted as the features of the machine-learning algorithms that aim to predict the structural damage after each seismic event. The structural analyses are performed considering both real and artificial ground motion sequences, while the structural damage is expressed in terms of two overall damage indices. The comparative study results in the most efficient damage index, as well as the most promising machine learning algorithm in predicting the structural response of a reinforced concrete building under single or multiple seismic events. Finally, the configured methodology is deployed in a user-friendly web application.Keywords: seismic sequence; machine learning algorithms; repeated earthquakes; structural damage prediction; intensity measures; damage accumulation; machine learning; artificial neural networ