Publishing histograms with$\epsilon$-differential privacyhas been studied extensively in the literature. Existing schemes aim at maximizing theutilityof the published data, while previous experimental evaluations analyze the privacy/utility trade-off. In this paper, we provide the first experimental evaluation of differentially private methods that goes beyond utility, emphasizing also on another important aspect, namelyefficiency. Towards this end, we first observe that all existing schemes are comprised of a small set of common blocks. We then optimize and choose the best implementation for each block, determine the combinations of blocks that capture the entire literature, and propose novel block combinations. We qualitatively assess the quality of the schemes based on the skyline of efficiency and utility, i.e., based on whether a method is dominated on both aspects or not. Using exhaustive experiments on four real datasets with different characteristics, we conclude that there are always trade-offs in terms of utility and efficiency. We demonstrate that the schemes derived from our novel block combinations provide the best trade-offs for time critical applications. Our work can serve as a guide to help practitionersengineera differentially private histogram scheme depending on their application requirements.

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.

TileDB is a system for managing data that are naturally represented as dense or sparse multi-dimensional arrays. TileDB's primary goal is massive scale, where data need to be stored persistently at a low cost, while allowing rapid access during parallel computation. Contrary to traditional data management systems, TileDB is an embeddable C library that can easily be integrated with various higher-level programming languages and scientific computing tools. It supports persistent storage on various backends such as POSIX filesystems, HDFS, Amazon S3, and more. TileDB has been successfully used in genomics as the storage engine of GenomicsDB, a project maintained by the Intel Health and Life Sciences group that is currently integrated with the Broad Institute's GATK4. This paper reviews the data model, architecture and key design principles of TileDB, and outlines our future vision for using TileDB in important scientific applications.

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 Databases 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 paper, 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. 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 analytically detail the superiority of our proposals over prior work and experimentally confirm their practicality.

We present a novel storage manager for multi-dimensional arrays that arise in scientific applications, which is part of a larger scientific data management system called TileDB. In contrast to existing solutions, TileDB is optimized for both dense and sparse arrays. Its key idea is to organize array elements into ordered collections called fragments. Each fragment is dense or sparse, and groups contiguous array elements into data tiles of fixed capacity. The organization into fragments turns random writes into sequential writes, and, coupled with a novel read algorithm, leads to very efficient reads. TileDB enables parallelization via multi-threading and multi-processing, offering thread-/process-safety and atomicity via lightweight locking. We show that TileDB delivers comparable performance to the HDF5 dense array storage manager, while providing much faster random writes. We also show that TileDB offers substantially faster reads and writes than the SciDB array database system with both dense and sparse arrays. Finally, we demonstrate that TileDB is considerably faster than adaptations of the Vertica relational column-store for dense array storage management, and at least as fast for the case of sparse arrays.

This paper presents BigDAWG, a reference implementation of a new architecture for "Big Data" applications. Such applications not only call for large-scale analytics, but also for real-time streaming support, smaller analytics at interactive speeds, data visualization, and cross-storage-system queries. Guided by the principle that "one size does not fit all", we build on top of a variety of storage engines, each designed for a specialized use case. To illustrate the promise of this approach, we demonstrate its effectiveness on a hospital application using data from an intensive care unit (ICU). This complex application serves the needs of doctors and researchers and provides real-time support for streams of patient data. It showcases novel approaches for querying across multiple storage engines, data visualization, and scalable real-time analytics.

We study the problem of authenticated multi-dimensional range queries over outsourced databases, where an owner outsources its database to an untrusted server, which maintains it and answers queries to clients. Previous schemes either scale exponentially in the number of query dimensions, or rely on heuristic data structures without provable bounds. Most importantly, existing work requires an exponential, in the database attributes, number of structures to support queries on every possible combination of dimensions in the database. In this paper, we propose the first schemes that (i) scale linearly with the number of dimensions, and (ii) support queries on any set of dimensions with linear in the number of attributes setup cost and storage. We achieve this through an elaborate fusion of novel and existing set-operation sub-protocols. We prove the security of our solutions relying on the q-Strong Bilinear Diffie-Hellman assumption, and experimentally confirm their feasibility.

Numerous applications require continuous publication of statistics or monitoring purposes, such as real-time traffic analysis, timely disease outbreak discovery, and social trends observation. These statistics may be derived from sensitive user data and, hence, necessitate privacy preservation. A notable paradigm for offering strong privacy guarantees in statistics publishing is ε-differential privacy. However, there is limited literature that adapts this concept to settings where the statistics are computed over an infinite stream of "events" (i.e., data items generated by the users), and published periodically. These works aim at hiding a single event over the entire stream. We argue that, in most practical scenarios, sensitive information is revealed from multiple events occurring at contiguous time instances. Towards this end, we put forth the novel notion of w - event privacy over infinite streams, which protects any event sequence occurring in w successive time instants. We first formulate our privacy concept, motivate its importance, and introduce a methodology for achieving it. We next design two instantiations, whose utility is independent of the stream length. Finally, we confirm the practicality of our solutions experimenting with real data.

We consider a stream outsourcing setting, where a data owner delegates the management of a set of disjoint data streams to an untrusted server. The owner authenticates his streams via signatures. The server processes continuous queries on the union of the streams for clients trusted by the owner. Along with the results, the server sends proofs of result correctness derived from the owner's signatures, which are verifiable by the clients. We design novel constructions for a collection of fundamental problems over streams represented as linear algebraic queries. In particular, our basic schemes authenticate dynamic vector sums, matrix products, and dot products. These techniques can be adapted for authenticating a wide range of important operations in streaming environments, including group-by queries, joins, in-network aggregation, similarity matching, and event processing. We also present extensions to address the case of sliding window queries, and when multiple clients are interested in different subsets of the data. These methods take advantage of a novel nonce chaining technique that we introduce, which is used to reduce the verification cost without affecting any other costs. All our schemes are lightweight and offer strong cryptographic guarantees derived from formal definitions and proofs. We experimentally confirm the practicality of our schemes in the performance-sensitive streaming setting.

We put forth the problem of delegating the evaluation of a pseudorandom function (PRF) to an untrusted proxy and introduce a novel cryptographic primitive called delegatable pseudorandom functions, or DPRFs for short: A DPRF enables a proxy to evaluate a pseudorandom function on a strict subset of its domain using a trapdoor derived from the DPRF secret key. The trapdoor is constructed with respect to a certain policy predicate that determines the subset of input values which the proxy is allowed to compute. The main challenge in constructing DPRFs is to achieve bandwidth efficiency (which mandates that the trapdoor is smaller than the precomputed sequence of the PRF values conforming to the predicate), while maintaining the pseudorandomness of unknown values against an attacker that adaptively controls the proxy. A DPRF may be optionally equipped with an additional property we call policy privacy, where any two delegation predicates remain indistinguishable in the view of a DPRF-querying proxy: achieving this raises new design challenges as policy privacy and bandwidth efficiency are seemingly conflicting goals.

We consider a stream outsourcing setting, where a data owner delegates the management of a set of disjoint data streams to an untrusted server. The owner authenticates his streams via signatures. The server processes continuous queries on the union of the streams for clients trusted by the owner. Along with the results, the server sends proofs of result correctness derived from the owner's signatures, which are easily verifiable by the clients. We design novel constructions for a collection of fundamental problems over streams represented as linear algebraic queries. In particular, our basic schemes authenticate dynamic vector sums and dot products, as well as dynamic matrix products. These techniques can be adapted for authenticating a wide range of important operations in streaming environments, including group by queries, joins, in-network aggregation, similarity matching, and event processing. All our schemes are very lightweight, and offer strong cryptographic guarantees derived from formal definitions and proofs. We experimentally confirm the practicality of our schemes.

The proliferation of GPS-enabledmobile devises and the popularity of social networking have recently led to the rapid growth of Geo-Social Networks (GeoSNs). GeoSNs have created a fertile ground for novel location-based social interactions and advertising. These can be facilitated by GeoSN queries, which extract useful information combining both the social relationships and the current location of the users. This paper constitutes the first systematic work on GeoSN query processing. We propose a general framework that offers flexible data management and algorithmic design. Our architecture segregates the social, geographical and query processing modules. Each GeoSN query is processed via a transparent combination of primitive queries issued to the social and geographical modules. We demonstrate the power of our framework by introducing several "basic" and "advanced" query types, and devising various solutions for each type. Finally, we perform an exhaustive experimental evaluation with real and synthetic datasets, based on realistic implementations with both commercial software (such as MongoDB) and state-of-the-art research methods. Our results confirm the viability of our framework in typical large-scale GeoSNs.

We address one-time publishing of non-overlapping counts with ε-differential privacy. These statistics are useful in a wide and important range of applications, including transactional, traffic and medical data analysis. Prior work on the topic publishes such statistics with prohibitively low utility in several practical scenarios. Towards this end, we present GS, a method that pre-processes the counts by elaborately grouping and smoothing them via averaging. This step acts as a form of preliminary perturbation that diminishes sensitivity, and enables GS to achieve ε-differential privacy through low Laplace noise injection. The grouping strategy is dictated by a sampling mechanism, which minimizes the smoothing perturbation. We demonstrate the superiority of GS over its competitors, and confirm its practicality, via extensive experiments on real datasets.

Computational Private Information Retrieval (cPIR) protocols allow a client to retrieve one bit from a database, without the server inferring any information about the queried bit. These protocols are too costly in practice because they invoke complex arithmetic operations for every bit of the database. In this paper, we present pCloud, a distributed system that constitutes the first attempt toward practical cPIR. Our approach assumes a disk-based architecture that retrieves one page with a single query. Using a striping technique, we distribute the database to a number of cooperative peers, and leverage their computational resources to process cPIR queries in parallel. We implemented pCloud on the PlanetLab network, and experimented extensively with several system parameters. Our results indicate that pCloud reduces considerably the query response time compared to the traditional client/server model, and has a very low communication overhead. Additionally, it scales well with an increasing number of peers, achieving a linear speedup.

In-network aggregation reduces the energy cost of processing aggregate queries (such as SUM, MAX, etc.) in wireless sensor networks. Recently, research has focused on secure in-network aggregation, motivated by the following two scenarios: 1) the sensors are deployed in open and unsafe environments, and 2) the aggregation process is outsourced to an untrustworthy service. Despite the bulk of work on the topic, there is currently no solution providing both integrity and confidentiality in the above scenarios. Moreover, existing solutions either return approximate results, or have limited applicability to certain types of aggregate queries. Our paper is the first work that provides both integrity and confidentiality in the aforementioned scenarios, while covering a wide range of aggregates and returning exact results. We initially present SIES, a scheme that solves exact SUM queries through a combination of homomorphic encryption and secret sharing. Subsequently, we show how to adapt SIES in order to support many other exact aggregate queries (such as MAX, MEDIAN, etc.). Finally, we augment our schemes with a functionality that identifies malicious sensors, preventing denial-of-service (DoS) attacks and attributing robustness to the system. Our techniques are lightweight and induce very small bandwidth consumption. Therefore, they constitute ideal solutions for resource-constrained sensors.

In-network aggregation is a popular methodology adopted in wireless sensor networks, which reduces the energy expenditure in processing aggregate queries (such as SUM, MAX, etc.) over the sensor readings. Recently, research has focused on secure in-network aggregation, motivated (i) by the fact that the sensors are usually deployed in open and unsafe environments, and (ii) by new trends such as outsourcing, where the aggregation process is delegated to an untrustworthy service. This new paradigm necessitates the following key security properties: data confidentiality, integrity, authentication, and freshness. The majority of the existing work on the topic is either unsuitable for large-scale sensor networks, or provides only approximate answers for SUM queries (as well as their derivatives, e.g., COUNT, AVG, etc). Moreover, there is currently no approach offering both confidentiality and integrity at the same time. Towards this end, we propose a novel and efficient scheme called SIES. SIES is the first solution that supports Secure In-network processing of Exact SUM queries, satisfying all security properties. It achieves this goal through a combination of homomorphic encryption and secret sharing. Furthermore, SIES is lightweight (it relies on inexpensive hash operations and modular additions/multiplications), and features a very small bandwidth consumption (in the order of a few bytes). Consequently, SIES constitutes an ideal method for resource-constrained sensors.

This paper studies the nearest keyword (NK) problem on XML documents. In general, the dataset is a tree where each node is associated with one or more keywords. Given a node q and a keyword w, an NK query returns the node that is nearest to q among all the nodes associated with w. NK search is not only useful as a stand-alone operator but also as a building brick for important tasks such as XPath query evaluation and keyword search. We present an indexing scheme that answers NK queries efficiently, in terms of both practical and worst-case performance. The query cost is provably logarithmic to the number of nodes carrying the query keyword. The proposed scheme occupies space linear to the dataset size, and can be constructed by a fast algorithm. Extensive experimentation confirms our theoretical findings, and demonstrates the effectiveness of NK retrieval as a primitive operator in XML databases.

Multistep processing is commonly used for nearest neighbor (NN) and similarity search in applications involving high-dimensional data and/or costly distance computations. Today, many such applications require a proof of result correctness. In this setting, clients issue NN queries to a server that maintains a database signed by a trusted authority. The server returns the NN set along with supplementary information that permits result verification using the data set signature. An adaptation of the multistep NN algorithm incurs prohibitive network overhead due to the transmission of false hits, i.e., records that are not in the NN set, but are nevertheless necessary for its verification. In order to alleviate this problem, we present a novel technique that reduces the size of each false hit. Moreover, we generalize our solution for a distributed setting, where the database is horizontally partitioned over several servers. Finally, we demonstrate the effectiveness of the proposed solutions with real data sets of various dimensionalities.

The tremendous growth of the Internet has significantly reduced the cost of obtaining and sharing information about individuals, raising many concerns about user privacy. Spatial queries pose an additional threat to privacy because the location of a query may be sufficient to reveal sensitive information about the querier. In this paper we focus on k nearest neighbor ( k NN) queries and define the notion of strong location privacy , which renders a query indistinguishable from any location in the data space. We argue that previous work fails to support this property for arbitrary k NN search. Towards this end, we introduce methods that offer strong location privacy, by integrating private information retrieval (PIR) functionality. Specifically, we employ secure hardware-aided PIR, which has been proven very efficient and is currently considered as a practical mechanism for PIR. Initially, we devise a benchmark solution building upon an existing PIR-based technique. Subsequently, we identify its drawbacks and present a novel scheme called AHG to tackle them. Finally, we demonstrate the performance superiority of AHG over our competitor, and its viability in applications demanding the highest level of privacy.

In the database outsourcing paradigm, a data owner (DO) delegates its DBMS administration to a specialized service provider (SP) that receives and processes queries from clients. The traditional outsourcing model (TOM) requires that the DO and the SP maintain authenticated data structures to enable authentication of query results. In this paper, we present SAE, a novel outsourcing model that separates authentication from query execution. Specifically, the DO does not perform any task except for maintaining its dataset (if there are updates). The SP only stores the DO's dataset and computes the query results using a conventional DBMS. All security-related tasks are outsourced to a separate trusted entity (TE), which maintains limited authentication information about the original dataset. A client contacts the TE when it wishes to establish the correctness of a result returned by the SP. The TE efficiently generates a verification token of negligible size. The client can verify the token with minimal cost. SAE eliminates the participation of the DO and the SP in the authentication process, and outperforms TOM in every aspect, including processing cost for all parties involved, communication overhead, query response time and ease of implementation in practical applications.

The vast majority of work on skyline queries considers totally ordered domains, whereas in many applications some attributes are partially ordered, as for instance, domains of set values, hierarchies, intervals and preferences. The only work addressing this issue has limited progressiveness and pruning ability, and it is only applicable to static skylines. This paper overcomes these problems with the following contributions: (i) we introduce a generic framework, termed TSS, for handling partially ordered domains using topological sorting. (ii) We propose a novel dominance check that eliminates false hits/misses, further enhancing progressiveness and pruning ability. (iii) We extend our methodology to dynamic skylines with respect to an input query. In this case, the dominance relationships change according to the query specification, and their computation is rather complex. We perform an extensive experimental evaluation demonstrating that TSS is up to 9 times and up to 2 orders of magnitude faster than existing methods in the static and the dynamic case, respectively.

Database outsourcing requires that a query server constructs a proof of result correctness, which can be verified by the client using the data owner's signature. Previous authentication techniques deal with range queries on a single relation using an authenticated data structure (ADS). On the other hand, authenticated join processing is inherently more complex than ranges since only the base relations (but not their combination) are signed by the owner. In this paper, we present three novel join algorithms depending on the ADS availability: (i) Authenticated Indexed Sort Merge Join (AISM), which utilizes a single ADS on the join attribute, (ii) Authenticated Index Merge Join (AIM) that requires an ADS (on the join attribute) for both relations, and (iii) Authenticated Sort Merge Join (ASM), which does not rely on any ADS. We experimentally demonstrate that the proposed methods outperform two benchmark algorithms, often by several orders of magnitude, on all performance metrics, and effectively shift the workload to the outsourcing service. Finally, we extend our techniques to complex queries that combine multi-way joins with selections and projections.

The embedding of positioning capabilities in mobile devices and the emergence of location-based applications have created novel opportunities for utilizing several types of multidimensional data through spatial outsourcing. In this setting, a data owner (DO) delegates its data management tasks to a location-based service (LBS) that processes queries originating from several clients/subscribers. Because the LBS is not the real owner of the data, it must prove (to each client) the correctness of query output using an authenticated structure signed by the DO. Currently there is very narrow selection of multidimensional authenticated structures, among which the VR-tree is the best choice. Our first contribution is the MR-tree, a novel index suitable for spatial outsourcing. We show, analytically and experimentally, that the MR-tree outperforms the VR-tree, usually by orders of magnitude, on all performance metrics, including construction cost, index size, query and verification overhead. Motivated by the fact that successive queries by the same mobile client exhibit locality, we also propose a synchronized caching technique that utilizes the results of previous queries to reduce the size of the additional information sent to the client for verification purposes.