Cloud business intelligence is an increasingly popular choice to deliver decision support capabilities via elastic, pay-per-use resources. However, data security issues are one of the top concerns when dealing with sensitive data. In this paper, we propose a novel approach for securing cloud data warehouses by flexible verifiable secret sharing, fVSS. Secret sharing encrypts and distributes data over several cloud service providers, thus enforcing data privacy and availability. fVSS addresses four shortcomings in existing secret sharing-based approaches. First, it allows refreshing the data warehouse when some service providers fail. Second, it allows on-line analysis processing. Third, it enforces data integrity with the help of both inner and outer signatures. Fourth, it helps users control the cost of cloud warehousing by balancing the load among service providers with respect to their pricing policies. To illustrate fVSS' efficiency, we thoroughly compare it with existing secret sharing-based approaches with respect to security features, querying power and data storage and computing costs.
Regarding accessing shares, classical secret sharing @cite_17 and most of its above-cited extensions natively support some exact match and aggregation operators, i.e., equality and inequality, sum, average and count. Each approach handles operators needing sorted data, e.g., range operators, maximum and minimum, with various techniques: preaggregation before sharing @cite_6 , a B++ tree index @cite_11 @cite_18 or the rank coefficient used in classical secret sharing @cite_15 @cite_1 @cite_3 . However, extra storage space is needed to store these data structures. Finally, almost all approaches allow updates on shares, since each piece of data is encrypted independently.
@cite_18 @cite_1 @cite_17 @cite_6 @cite_3 @cite_15 @cite_11
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We consider the problem of threshold secret sharing in groups with hierarchical structure. In such settings, the secret is shared among a group of participants that is partitioned into levels. The access structure is then determined by a sequence of threshold requirements: a subset of participants is authorized if it has at least k0 0 members from the highest level, as well as at least k1 > k0 members from the two highest levels and so forth. Such problems may occur in settings where the participants differ in their authority or level of confidence and the presence of higher level participants is imperative to allow the recovery of the common secret. Even though secret sharing in hierarchical groups has been studied extensively in the past, none of the existing solutions addresses the simple setting where, say, a bank transfer should be signed by three employees, at least one of whom must be a department manager. We present a perfect secret sharing scheme for this problem that, unlike most secret sharing schemes that are suitable for hierarchical structures, is ideal. As in Shamir's scheme, the secret is represented as the free coefficient of some polynomial. The novelty of our scheme is the usage of polynomial derivatives in order to generate lesser shares for participants of lower levels. Consequently, our scheme uses Birkhoff interpolation, i.e., the construction of a polynomial according to an unstructured set of point and derivative values. A substantial part of our discussion is dedicated to the question of how to assign identities to the participants from the underlying finite field so that the resulting Birkhoff interpolation problem will be well posed. In addition, we devise an ideal and efficient secret sharing scheme for the closely related hierarchical threshold access structures that were studied by Simmons and Brickell. In this paper, we propose the new (t i , n) threshold multi secret sharing scheme. In our scheme, each user keeps only one share and the secret vectors K i = k i , 1 ,…, k i , mi are shared according to independent (t i , n) threshold access structures (i = l,…, h). Here note that a (t i , n) threshold multi-secret sharing scheme has been proposed by [1]. Unfortunately, however, there is a serious attack on Shi's scheme that can reconstruct all elements of K i with only t 1 shares. In this paper, we shall give the solution to this problem by using one-way functions. Furthermore, Shi's scheme has a drawback such that the dimension of the secret vector K i cannot exceed t i . On the other hand, our scheme has no such a restriction. Finally, we demonstrate that our scheme is more efficient than Shi's scheme by showing that our scheme is more efficient than the three naive improvements on the protocol of In the classical privacy amplification problem Alice and Bob share information that is only partially secret towards an eavesdropper Charlie. Their goal is to distill this information to a shorter string that is completely secret. The classical privacy amplification problem can be solved almost optimally using extractors. An interesting variant of the problem, where the eavesdropper Charlie is allowed to keep quantum information rather than just classical information, was introduced by Konig, Maurer and Renner. In this setting, the eavesdropper Charlie may entangle himself with the input (without changing it) and the only limitation Charlie has is that it may keep at most b qubits of storage. A natural question is whether there are classical extractors that are good even against quantum storage. Recent work has shown that some classical extractors miserably fail against quantum storage. At the same time, it was shown that some other classical extractors work well even against quantum storage, but all these extractors had a large seed length that was either as large as the extractor output, or as large as the quantum storage available to the eavesdropper. In this paper we show that a modified version of Trevisan's extractor is good even against quantum storage, thereby giving the first such construction with logarithmic seed length. The technique we use is a combination of Trevisan's approach of constructing an extractor from a black-box pseudorandom generator, together with locally list-decodable codes and previous work done on quantum random access codes. The nearestor near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search index structures for performing similarity search over high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. Unfortunately, all known techniques for solving this problem fall prey to the of dimensionality." That is, the data structures scale poorly with data dimensionality; in fact, if the number of dimensions exceeds 10 to 20, searching in k-d trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than brute-force linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather heuristic, determining an approximate nearest neighbor should su ce for most practical purposes. In this paper, we examine a novel scheme for approximate similarity search based on hashing. The basic idea is to hash the points Supported by NAVY N00014-96-1-1221 grant and NSF Grant IIS-9811904. Supported by Stanford Graduate Fellowship and NSF NYI Award CCR-9357849. Supported by ARO MURI Grant DAAH04-96-1-0007, NSF Grant IIS-9811904, and NSF Young Investigator Award CCR9357849, with matching funds from IBM, Mitsubishi, Schlumberger Foundation, Shell Foundation, and Xerox Corporation. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the VLDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Data Base Endowment. To copy otherwise, or to republish, requires a fee and or special permission from the Endowment. Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, 1999. from the database so as to ensure that the probability of collision is much higher for objects that are close to each other than for those that are far apart. We provide experimental evidence that our method gives signi cant improvement in running time over other methods for searching in highdimensional spaces based on hierarchical tree decomposition. Experimental results also indicate that our scheme scales well even for a relatively large number of dimensions (more than 50). In a secret sharing scheme, a dealer has a secret. The dealer gives each participant in the scheme a share of the secret. There is a set Γ of subsets of the participants with the property that any subset of participants that is in Γ can determine the secret. In a perfect secret sharing scheme, any subset of participants that is not in Γ cannot obtain any information about the secret. We will say that a perfect secret sharing scheme is ideal if all of the shares are from the same domain as the secret. Shamir and Blakley constructed ideal threshold schemes, and Benaloh has constructed other ideal secret sharing schemes. In this paper, we construct ideal secret sharing schemes for more general access structures which include the multilevel and compartmented access structures proposed by Simmons. We consider the rational versions of two of the classical problems in foundations of cryptography: secret sharing and multiparty computation, suggested by Halpern and Teague (STOC 2004). Our goal is to design games and fair strategies that encourage rational participants to exchange information about their inputs for their mutual benefit, when the only mean of communication is a broadcast channel. We show that protocols for the above information exchanging tasks, where players' values come from a bounded domain, cannot satisfy some of the most desirable properties. In contrast, we provide a rational secret sharing scheme with simultaneous broadcast channel in which shares are taken from an unbounded domain, but have finite (and polynomial sized) expectation. Previous schemes (mostly cryptographic) have required computational assumptions, making them inexact and susceptible to backward induction, or used stronger communication channels. Our scheme is non-cryptographic, immune to backward induction, and satisfies a stronger rationality concept (strict Nash equilibrium). We show that our solution can also be used to construct an e-Nash equilibrium secret sharing scheme for the case of a non-simultaneous broadcast channel. We investigate a new approach to the design of distributed, shared-nothing RDF engines. Our engine, coined "TriAD", combines join-ahead pruning via a novel form of RDF graph summarization with a locality-based, horizontal partitioning of RDF triples into a grid-like, distributed index structure. The multi-threaded and distributed execution of joins in TriAD is facilitated by an asynchronous Message Passing protocol which allows us to run multiple join operators along a query plan in a fully parallel, asynchronous fashion. We believe that our architecture provides a so far unique approach to join-ahead pruning in a distributed environment, as the more classical form of sideways information passing would not permit for executing distributed joins in an asynchronous way. Our experiments over the LUBM, BTC and WSDTS benchmarks demonstrate that TriAD consistently outperforms centralized RDF engines by up to two orders of magnitude, while gaining a factor of more than three compared to the currently fastest, distributed engines. To our knowledge, we are thus able to report the so far fastest query response times for the above benchmarks using a mid-range server and regular Ethernet setup. Researchers have been studying security challenges of database outsourcing for almost a decade. Privacy of outsourced data is one of the main challenges when the “Database As a Service” model is adopted in the service oriented trend of the cloud computing paradigm. This is due to the insecurity of the network environment or even the untrustworthiness of the service providers. This paper proposes a method to preserve privacy of outsourced data based on Shamir’s secret sharing scheme. We split attribute values into several parts and distribute them among untrusted servers. The problem of using secret sharing in data outsourcing scenario is how to search efficiently within the randomly generated pool of shares. In this paper, at first, we customize Shamir’s scheme to have A Searchable Secret Sharing Scheme (AS4) that enables the efficient execution of different kinds of queries over distributed shares. Then, we extend our method for sharing values to A Secure Searchable Secret Sharing Scheme (AS5) to tolerate statistical attacks based on adversary’s knowledge about outsourced data distribution. In AS5 data shares are generated uniformly across a domain to prevent information leakage about the outsourced data. Abstract Sharing information is useful in specialising, optimising and parallelising logic programs and thus sharing analysis is an important topic of both abstract interpretation and logic programming. Sharing analyses infer which pairs of program variables can never be bound to terms that contain a common variable. We generalise a classic pair-sharing analysis from Herbrand unification to trace sharing over rational tree constraints. This is useful for reasoning about programs written in SICStus and Prolog-III because these languages use rational tree unification as the default equation solver. Abstract In this paper, we proposes high payload reversible data hiding scheme using weighted matrix. Generate cover image by enlarge the size of original image using interpolation. Partition the original image into (3 × 3) pixel blocks and cover image into (5 × 5) blocks. Perform modular sum of entry-wise multiplication of an (3 × 3) original pixel block with same size shared predefined weighted matrix W . At most twelve multiplication operations are performed to embed 48 bits secret data within an (5 × 5) overlapped pixel block. In each operation, the data embedding position are identified and stored the positional value by modifying three least significant bits of the interleaved pixel of cover image to hide large amount of secret data. For i -th block i = 1, 2, …, we update the weighted matrix W i +1 as W i +1 = ( W i × κ − 1) mod 9, where gcd ( κ , 9) = 1. The original pixels are not effected during data embedding in our scheme which assure reversibility. The proposed scheme provides average embedding payload 2.97 bits per pixel (bpp) with good visual quality measured by peak signal to noise ratio (PSNR) guaranteed to be higher than 37.97 dB. Finally, we compare our scheme with other state-of-the-art methods and obtain reasonably better performance in terms of data embedding capacity. We study a class of aggregate-join queries with multiple aggregation operators evaluated over annotated relations. We show that straightforward extensions of standard multiway join algorithms and generalized hypertree decompositions (GHDs) provide best-known runtime guarantees. In contrast, prior work uses bespoke algorithms and data structures and does not match these guarantees. Our extensions to the standard techniques are a pair of simple tests that (1) determine if two orderings of aggregation operators are equivalent and (2) determine if a GHD is compatible with a given ordering. These tests provide a means to find an optimal GHD that, when provided to standard join algorithms, will correctly answer a given aggregate-join query. The second class of our contributions is a pair of complete characterizations of (1) the set of orderings equivalent to a given ordering and (2) the set of GHDs compatible with some equivalent ordering. We show by example that previous approaches are incomplete. The key technical consequence of our characterizations is a decomposition of a compatible GHD into a set of (smaller) unconstrained GHDs, i.e. into a set of GHDs of sub-queries without aggregations. Since this decomposition is comprised of unconstrained GHDs, we are able to connect to the wide literature on GHDs for join query processing, thereby obtaining improved runtime bounds, MapReduce variants, and an efficient method to find approximately optimal GHDs. We study a class of aggregate-join queries with multiple aggregation operators evaluated over annotated relations. We show that straightforward extensions of standard multiway join algorithms and generalized hypertree decompositions (GHDs) provide best-known runtime guarantees. In contrast, prior work uses bespoke algorithms and data structures and does not match these guarantees. We extend the standard techniques by providing a complete characterization of (1) the set of orderings equivalent to a given ordering and (2) the set of GHDs valid with respect to the given ordering, i.e., GHDs that correctly answer a given aggregate-join query when provided to (simple variants of) standard join algorithms. We show by example that previous approaches are incomplete. The key technical consequence of our characterizations is a decomposition of a valid GHD into a set of (smaller) unconstrained GHDs, i.e., into a set of GHDs of sub-queries without aggregations. Since this decomposition is comprised of unconstrained GHDs, we are able to connect to the wide literature on GHDs for join query processing, thereby obtaining improved runtime bounds, MapReduce variants, and an efficient method to find approximately optimal GHDs. Highlights? We propose an efficient (n, t, n)-VSS. ? We propose a (n, t, n)-MSS. ? We propose a (n, t, n)-VMSS. ? All our (n, t, n) SS are unconditionally secure. Recently, Harn and Lin introduced a notion of strong t-consistency of a (t, n) secret sharing scheme and proposed a strong (n, t, n) verifiable secret sharing (VSS). In this paper, we propose a strong (n, t, n) VSS which is more efficient than Harn and Lin's VSS. Using the same approach, we propose a (n, t, n) multi-secret sharing scheme (MSS) to allow shareholders to share n-t+1 secrets. Also, the proposed (n, t, n) MSS can be modified to include the verifiable feature. All proposed schemes are unconditionally secure and are based on Shamir's (t, n) secret sharing scheme. A secret sharing scheme is a method to store information securely and reliably. Particularly, in a threshold secret sharing scheme , a secret is encoded into @math shares, such that any set of at least @math shares suffice to decode the secret, and any set of at most @math d @math t_ 1 d n$ . The scheme is based on a generalization of Shamir’s secret sharing scheme and preserves its simplicity and efficiency. In addition, we consider the setting of secure distributed storage where the proposed communication efficient secret sharing schemes not only improve decoding bandwidth but further improve disk access complexity during decoding. The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain "identifying" attributes) contains at least k records. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of l-diversity has been proposed to address this; l-diversity requires that each equivalence class has at least l well-represented values for each sensitive attribute. In this paper we show that l-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. We propose a novel privacy notion called t-closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). We choose to use the earth mover distance measure for our t-closeness requirement. We discuss the rationale for t-closeness and illustrate its advantages through examples and experiments. We formulate and study a privacy guarantee to data owners, who share information with clients by publishing views of a proprietary database. The owner identi.es the sensitive proprietary data using a secret query against the proprietary database. Given an extra view, the privacy guarantee ensures that potential attackers will not learn any information about the secret that could not already be obtained from the existing views. We de.ne “learning” as the modi.cation of the attacker's a-priori probability distribution on the set of possible secrets. We assume arbitrary a-priori distributions (including distributions that correlate the existence of particular tuples) and solve the problem when secret and views are expressed as unions of conjunctive queries with non-equalities, under integrity constraints. We consider guarantees (a) for given view extents (b) for given domain of the secret and (c) independent of the domain and extents. We critically evaluate the current state of research in multiple query opGrnization, synthesize the requirements for a modular opCrnizer, and propose an architecture. Our objective is to facilitate future research by providing modular subproblems and a good general-purpose data structure. In rhe context of this archiuzcture. we provide an improved subsumption algorithm. and discuss migration paths from single-query to multiple-query oplimizers. The architecture has three key ingredients. First. each type of work is performed at an appropriate level of abstraction. Segond, a uniform and very compact representation stores all candidate strategies. Finally, search is handled as a discrete optimization problem separable horn the query processing tasks. 1. Problem Definition and Objectives A multiple query optimizer (h4QO) takes several queries as input and seeks to generate a good multi-strategy, an executable operator gaph that simultaneously computes answers to all the queries. The idea is to save by evaluating common subexpressions only once. The commonalities to be exploited include identical selections and joins, predicates that subsume other predicates, and also costly physical operators such as relation scans and SOULS. The multiple query optimization problem is to find a multi-strategy that minimizes the total cost (with overlap exploited). Figure 1 .l shows a multi-strategy generated exploiting commonalities among queries Ql-Q3 at both the logical and physical level. To be really satisfactory, a multi-query optimization algorithm must offer solution quality, ejjiciency, and ease of Permission to copy without fee all a part of this mataial is granted provided that the copies are nut made a diitributed for direct commercial advantage, the VIDB copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Very Large Da Blse Endowment. To copy otherwise. urturepublim identify many kinds of commonalities (e.g., by predicate splitting, sharing relation scans); and search effectively to choose a good combination of l-strategies. Efficiency requires that the optimization avoid a combinatorial explosion of possibilities, and that within those it considers, redundant work on common subexpressions be minimized. ‘Finally, ease of implementation is crucial an algorithm will be practically useful only if it is conceptually simple, easy to attach to an optimizer, and requires relatively little additional soft- The k-anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain “identifying” attributes) contains at least k records. Recently, several authors have recognized that k-anonymity cannot prevent attribute disclosure. The notion of l-diversity has been proposed to address this; l-diversity requires that each equivalence class has at least l well-represented (in Section 2) values for each sensitive attribute. In this paper, we show that l-diversity has a number of limitations. In particular, it is neither necessary nor sufficient to prevent attribute disclosure. Motivated by these limitations, we propose a new notion of privacy called “closeness.” We first present the base model t-closeness, which requires that the distribution of a sensitive attribute in any equivalence class is close to the distribution of the attribute in the overall table (i.e., the distance between the two distributions should be no more than a threshold t). We then propose a more flexible privacy model called (n,t)-closeness that offers higher utility. We describe our desiderata for designing a distance measure between two probability distributions and present two distance measures. We discuss the rationale for using closeness as a privacy measure and illustrate its advantages through examples and experiments. Secret sharing and multiparty computation (also called "secure function evaluation") are fundamental primitives in modern cryptography, allowing a group of mutually distrustful players to perform correct, distributed computations under the sole assumption that some number of them will follow the protocol honestly. This paper investigates how much trust is necessary -- that is, how many players must remain honest -- in order for distributed quantum computations to be possible. We present a verifiable quantum secret sharing (VQSS) protocol, and a general secure multiparty quantum computation (MPQC) protocol, which can tolerate any [ n - 1 2 ] cheaters among n players. Previous protocols for these tasks tolerated [ n - 1 4 ] and [ n - 1 6 ] cheaters, respectively. The threshold we achieve is tight -- even in the classical case, "fair" multiparty computation is not possible if any set of n 2 players can cheat. Our protocols rely on approximate quantum errorcorrecting codes, which can tolerate a larger fraction of errors than traditional, exact codes. We introduce new families of authentication schemes and approximate codes tailored to the needs of our protocols, as well as new state purification techniques along the lines of those used in faulttolerant quantum circuits. We consider the classical secret sharing problem in the case where all agents are selfish but rational. In recent work, Kol and Naor show that in the non-simultaneous communciation model (i.e. when rushing is possible), there is no Nash equilibrium that ensures all agents learn the secret. However, they describe a mechanism for this problem that is an e-Nash equilibrium, i.e. it is close to an equilibrium in the sense that no player can gain more than e utility by deviating from it. Unfortunately, the Kol and Naor mechanism, and, to the best of our knowledge, all previous mechanisms for this problem require each agent to send O(n) messages in expectation, where n is the number of agents. This may be problematic for some applications of rational secret sharing such as secure multiparty computation and simulation of a mediator. We address this issue by describing a mechanism for rational n-out-of-n secret sharing that is an e-Nash equilibrium, and is scalable in the sense that it requires each agent to send only an expected O(1) bits. Moreover, the latency of our mechanism is O(log n) in expectation, compared to O(n) expected latency for the Kol and Naor result. We also design mechanisms for a relaxed variant of rational m-out-of-n secret sharing where m = Θ(n) that require each processor to send O(log n) bits and have O( n) latency. Our mechanisms are non-cryptographic, and are not susceptible to backwards induction.