Advanced data-intensive services and applications in IoT scenarios require efficient access to external resources for data processing and computation. Fog computing addresses such a need by moving part of the activity closer to the edge, alleviating network congestion and improving responsiveness. A major problem in scheduling the workflow required by IoT applications for their execution in fog nodes is accounting for the many Quality of Service requirements that should be guaranteed, as well as possible deadlines for workflow completion imposed by the applications. In this paper, we present a novel meta-heuristic approach for solving a deadline-constrained many-objective workflow scheduling problem that enhances the Arithmetic Optimization Algorithm with dynamic evolutionary state estimation for selecting arithmetic operators and balancing exploration and exploitation in the search space. Our approach also accounts for parallelization in task allocation and includes a repairing mechanism for infeasible solutions. The extensive experimental evaluation on benchmarks of real-world workflows, comparing against alternative algorithms, demonstrates the effectiveness of our approach.

Decentralized storage architectures are emerging as valid complementary solutions to cloud-based storage services. InterPlanetary File System (IPFS) is one of the most well-known distributed file storage protocols with wide adoption, good performance, and a variety of applications built over it. However, IPFS does not natively support data confidentiality and its decentralized nature limits the ability of data owners to maintain control on their resources and to force their deletion. We propose Mix-IPFS, an approach that allows data owners to maintain control on their resources uploaded to IPFS, guaranteeing their confidentiality and supporting secure deletion. Mix-IPFS is based on AONT encryption, which has the nice property of preventing decryption if the whole ciphertext is not available. Data owners can permanently delete a resource by making a small portion of its encrypted representation unavailable. Our solution uses a virtual file system to guarantee transparency to data owners (i.e., they can operate on plaintext resources). The experimental evaluation shows that the overhead of our approach is negligible (less than 2% for both upload and access operations).

We address the problem of indexing encrypted data outsourced to an external cloud server to support server-side execution of multi-attribute queries. Our approach partitions the dataset in groups with the same number of tuples, and associates all tuples in a group with the same combination of index values, so to guarantee protection against static inferences. Our indexing approach does not require any modifications to the server-side software stack, and requires limited storage at the client for query support. The experimental evaluation considers, for the storage of the encrypted and indexed dataset, both a relational database (PostgreSQL) and a key-value database (Redis). We carried out extensive experiments evaluating client-storage requirements and query performance. The experimental results confirm the efficiency of our solution. The proposal is supported by an open source implementation.

A complex problem when outsourcing data to the cloud is access control management. Encryption, by wrapping data with a self-enforcing protection layer, provides access control enforcement by making resources intelligible only to users holding the necessary key. The real challenge becomes then the efficient revocation of access. We address this challenge and present an approach to effectively and efficiently enforce access revocation on resources stored at external cloud providers. The approach relies on a resource transformation that provides strong mutual inter-dependency in its encrypted representation. To revoke access on a resource, it is then sufficient to update a small portion of it, with the guarantee that the resource as a whole (and any portion of it) will become unintelligible to those from whom access is revoked. Our experimental results show the effectiveness of our approach, and confirm its efficiency, especially when managing large resources with dynamic access policy.

More and more organizations are today using the cloud for their business as a convenient alternative to in-house solutions for storing, processing, and managing data. Cloud-based solutions are then permeating almost all aspects of business organizations, resulting appealing also for sensitive or security critical applications, whose enforcement in the cloud requires however particular care. In this article, we provide an approach for securely relying on cloud-based services for the enforcement of Internal Controls and Audit (ICA) functions for corporate governance. Our approach builds on a formalization of the ICA process and its requirements and on the consideration of the protection guarantees to be provided when outsourcing the process to external cloud services. The enforcement of the requirements leverages the use of selective encryption providing a self-protection layer on the data and on ICA reports, the hierarchical organization of keys based on the organizational structure, and compact tags for regulating write operations. Our solution enables the management of the ICA process with cloud-based services, while ensuring satisfaction of the protection requirements.

The availability of a multitude of data sources has naturally increased the need for subjects to collaborate for supporting distributed computations that combine different data collections for their elaboration and analysis. Due to the quick pace at which datasets grow, often the authorities collecting and owning such datasets resort to external third parties (e.g., cloud providers) for their storage and management. Data under the control of different authorities are autonomously encrypted (using different encryption schemes and keys) for their external storage. This makes distributed computations combining these sources difficult to support. In this paper, we propose an approach enabling collaborative computations over data encrypted in storage, selectively involving also subjects that might not be authorized for accessing the data in plaintext when their collaboration is considered economically convenient. We also consider the possible adoption of trusted hardware components, to enable the evaluation of operations over plaintext data at non-fully trusted computational providers. The experimental results confirm the economic benefits that can be enabled by our proposal.

$\kappa $-Anonymity and$\ell $-diversity are two well-known privacy metrics that guarantee protection of the respondents of a dataset by obfuscating information that can disclose their identities and sensitive information. Existing solutions for enforcing them implicitly assume to operate in a centralized scenario, since they require complete visibility over the dataset to be anonymized, and can therefore have limited applicability in anonymizing large datasets. In this paper, we propose a solution that extends Mondrian (an efficient and effective approach designed for achieving$\kappa $-anonymity) for enforcing both$\kappa $-anonymity and$\ell $-diversity over large datasets in a distributed manner, leveraging the parallel computation of multiple workers. Our approach efficiently distributes the computation among the workers, without requiring visibility over the dataset in its entirety. Our data partitioning limits the need for workers to exchange data, so that each worker can independently anonymize a portion of the dataset. We implemented our approach providing parallel execution on a dynamically chosen number of workers. The experimental evaluation shows that our solution provides scalability, while not affecting the quality of the resulting anonymization.

Distributed computing supports large scale and data-intensive computations with the cooperation of a multitude of parties, each responsible for a portion of the workload. Such parties are often not fully reliable and may return incorrect results. In this article, we address the problem of assessing the integrity of the computation results. We provide a comprehensive characterization of two techniques,sentinelsandtwins, evaluating their effectiveness and synergy. Sentinels are pre-computed tasks whose result is known apriori, and enable checking returned results against a ground truth. Twins are replicated tasks assigned to different workers, and enable cross-checking returned results for a same task. The analysis considers many questions that arise in the design of a concrete integrity assessment strategy and identifies the parameters that have a critical impact on the overall protection. Our model enables to tune the integrity controls so to achieve best effectiveness. The model can be applied to a variety of scenarios and offers guidelines that can find extensive application.

We present an approach for indexing encrypted data stored at external providers to enable provider-side evaluation of queries. Our approach supports the evaluation of point and range conditions on multiple attributes. Protection against inferences from indexes is guaranteed by clustering tuples in boxes that are then mapped to the same index values, so to ensure collisions for individual attributes as well as their combinations. Our spatial-based algorithm partitions tuples to produce such a clustering in a way to ensure efficient query execution. Query translation and processing require the client to store a compact map. The experiments, evaluating query performance and client-storage requirements, confirm the efficiency enjoyed by our solution.

The combined adoption of selective encryption and smart contracts deployed on blockchains allows data owners to maintain control over their data when traded on digital data market platforms.Selective encryption, combined with key derivation techniques, guarantees that only customers who are entitled to access a resource can read its content.The adoption of smart contracts deployed on a blockchain permits to regulate the interplay among parties, the possible economic incentives to be paid to the owners, and the exchange of the information necessary for resource decryption (i.e., updates to the key derivation structure) upon payment.However, operations on blockchains have a cost.In this paper, we propose two approaches for updating the key derivation structure to enable customers to access resources, while limiting access times to resources and the cost of write operations on the blockchain to enforce purchases.

When moving large and heterogeneous data collections to the cloud, a key requirement concerns the selection of the most suitable (set of) cloud service(s) for outsourcing. Not only can different resources have different characteristics and requirements, but different cloud providers can also offer different services and security guarantees, and can have different costs. Selecting a single service for outsourcing an entire data collection can result in a non-optimal solution, as a single service satisfying, at reasonable costs, all the requirements specified by the data owner might not exist. Selecting a set of services could instead ensure the satisfaction of the requirements, possibly with economic advantages. In this article, we address this problem and present a flexible and expressive, yet simple model for supporting data owners in identifying a proper allocation of their resources to a set of cloud services. Our model allows data owners to specify in an easy and intuitive way protection requirements operating at the granularity level of single resource (or class thereof), and representing the minimum security guarantees that a cloud service must offer to store resources. Resources can be outsourced in plaintext or encrypted form, depending on their requirements and on what is the most convenient allocation. Data owners can then also specify global allocation requirements that apply to the overall allocation, to reduce the burden on their side and to avoid excessive fragmentation of the resource collection. We solve the problem of finding an allocation that satisfies both the protection and the global allocation requirements, while minimizing economic costs, by formulating it as a binary programming problem, thus allowing the use of existing techniques for its efficient solution.

With the cloud emerging as a successful paradigm for conveniently storing, accessing, processing, and sharing information, the cloud market has seen an incredible growth. An ever-increasing number of providers offer today several cloud plans, with different guarantees in terms of service properties such as performance, cost, or security. While such a variety naturally corresponds to a diversified user demand, it is far from trivial for users to identify the cloud providers and plans that better suit their specific needs. In this paper, we address the problem of supporting users in cloud plan selection. We characterize different kinds of requirements that may need to be supported in cloud plan selection and introduce a very simple and intuitive, yet expressive, language that captures different requirements as well as preferences users may wish to express. The corresponding formal modeling permits to reason on requirements satisfaction to identify plans that meet the constraints imposed by requirements, and to produce a preference-based ranking among such plans.

More and more organizations are today using the cloud for their business as a quite convenient alternative to in-house solutions for storing, processing, and managing data. Cloud-based solutions are then permeating almost all aspects of business organizations, resulting appealing also for functions that, already in-house, may result sensitive or security critical, and whose enforcement in the cloud requires then particular care. In this paper, we provide an approach for securely relying on cloud-based services for the enforcement of Internal Controls and Audit (ICA) functions for corporate governance. Our approach is based on the use of selective encryption and of tags to provide a level of self-protection to data and for enabling only authorized parties to access data and perform operations on them, providing privacy and integrity guarantees, as well as accountability and non-repudiation.

Decentralized cloud storage services represent a promising opportunity for a different cloud market, meeting the supply and demand for IT resources of an extensive community of users. The dynamic and independent nature of the resulting infrastructure introduces security concerns that can represent a slowing factor toward the realization of such an opportunity, otherwise clearly appealing and promising for the expected economic benefits. In this paper, we present an approach enabling resource owners to effectively protect and securely delete their resources while relying on decentralized cloud services for their storage. Our solution combines All-Or-Nothing-Transform for strong resource protection, and carefully designed strategies for slicing resources and for their decentralized allocation in the storage network. We address both availability and security guarantees, jointly considering them in our model and enabling resource owners to control their setting.

We propose an approach for allowing data owners to trade their data in digital data market scenarios, while keeping control over them. Our solution is based on a combination of selective encryption and smart contracts deployed on a blockchain, and ensures that only authorized users who paid an agreed amount can access a data item. We propose a safe interaction protocol for regulating the interplay between a data owner and subjects wishing to purchase (a subset of) her data, and an audit process for counteracting possible misbehaviors by any of the interacting parties. Our solution aims to make a step towards the realization of data market platforms where owners can benefit from trading their data while maintaining control.

Decentralized Cloud Storage (DCS) networks represent an interesting solution for data storage and management. DCS networks rely on the voluntary effort of a considerable number of (possibly untrusted) nodes, which may dynamically join and leave the network at any time. To profitably rely on DCS for data storage, data owners therefore need solutions that guarantee confidentiality and availability of their data. In this paper, we present an approach enabling data owners to keep data confidentiality and availability under control, limiting the owners intervention with corrective actions when availability or confidentiality is at risk. Our approach is based on the combined adoption of AONT (All-Or-Nothing-Transform) and fountain codes. It provides confidentiality of outsourced data also against malicious coalitions of nodes, and guarantees data availability even in case of node failures. Our experimental evaluation clearly shows the benefits of using fountain codes with respect to other approaches adopted by current DCS networks.

Cloud computing is the reference paradigm to provide data storage and management in a convenient and scalable manner. However, moving data to the cloud raises several issues, including the confidentiality of data and of accesses that are no more under the direct control of the data owner. The shuffle index has been proposed as a solution for addressing these issues when data are stored at an external third party. In this paper, we extend the shuffle index with support for access control, that is, for enforcing authorizations on data. Our approach is based on the use of selective encryption and on the organization of data and authorizations in two shuffle indexes. Owners regulate access to their data through authorizations that allow different users to access different portions of the data, while, at the same time, the confidentiality of accesses is guaranteed. The proposed approach also supports update operations over the outsourced data collection (i.e., insertion, removal, and update) as well as of the access control policy (i.e., grant and revoke). Also, our approach protects the nature of each access operation, making revoke operations and resource removal operations indistinguishable by the storing server and/or observing users.

We propose an approach to protect confidentiality of data and accesses to them when data are stored and managed by external providers, and hence not under direct control of their owner. Our approach is based on the use of distributed data allocation among three independent servers and on a dynamic re-allocation of data at every access. Dynamic re-allocation is enforced by swapping data involved in an access across the servers in such a way that accessing a given node implies re-allocating it to a different server, then destroying the ability of servers to build knowledge by observing accesses. The use of three servers provides uncertainty, to the eyes of the servers, of the result of the swapping operation, even in presence of collusion among them.

The distributed shuffle index strengthens the guarantees of access confidentiality provided by the shuffle index through the distribution of data among three cloud providers. In this paper, we analyze architectural and design issues and describe an implementation of the distributed shuffle index integrated with different cloud providers (i.e., Amazon S3, OpenStack Swift, Google Cloud Storage, and EMC Elastic Cloud Storage). The experimental results obtained with our implementation confirm the protection guarantees provided by the distributed shuffle index and its limited performance overhead, demonstrating its practical applicability in cloud scenarios.

We present a novel approach for the specification and enforcement of authorizations that enables controlled data sharing for collaborative queries in the cloud. Data authorities can establish authorizations regulating access to their data distinguishing three visibility levels (no visibility, encrypted visibility, and plaintext visibility). Authorizations are enforced in the query execution by possibly restricting operation assignments to other parties and by adjusting visibility of data on-the-fly. Our approach enables users and data authorities to fully enjoy the benefits and economic savings of the competitive open cloud market, while maintaining control over data.

We present an approach to enforce access revocation on resources stored at external cloud providers. The approach relies on a resource transformation that provides strong mutual inter-dependency in its encrypted representation. To revoke access on a resource, it is then sufficient to update a small portion of it, with the guarantee that the resource as a whole (and any portion of it) will become unintelligible to those from whom access is revoked. The extensive experimental evaluation on a variety of configurations confirmed the effectiveness and efficiency of our solution, which showed excellent performance and compatibility with several implementation strategies.

We present a novel approach for guaranteeing access privacy to data stored at an external cloud provider. Our solution relies on the grouping of resources into buckets then organized with a binary search tree. The tree is built on an index computed in a non-invertible non-order preserving way, and supports efficient key-based retrieval. Our approach to provide access privacy builds on this data organization providing uniform observability to the server in access execution and dynamically changing not only the physical storage allocation, but also the logical structure itself. Our analysis and experimental evaluation show the effectiveness of our approach.

Cloud computing is receiving massive interest from users and companies for its convenient support of scalable access to data and services. The variety and diversification of offers by cloud providers allow users to selectively adopt storage and computational services as they best suit their needs, including cost saving considerations. In such an open context, security remains a major concern, as confidentiality and integrity of data and queries over them can be at risk. In this paper, we present efficient techniques to verify the integrity of join queries computed by potentially untrusted cloud providers, while also protecting data and computation confidentiality. Our techniques support joins among multiple data sources and introduce a limited overhead in query computation, enabling also economical savings, as the ability to assess integrity increases the spectrum of offers that can be considered for performing the computation. Formal analysis and experimental evaluations confirm the effectiveness and efficiency of our solutions.

Data fragmentation has been proposed as a solution for protecting the confidentiality of sensitive associations when releasing data for publishing or external storage. To enrich the utility of data fragments, a recent approach has put forward the idea of complementing a pair of fragments with some (non-precise, hence loose) information on the association between them. Starting from the observation that in presence of multiple fragments the publication of several independent associations between pairs of fragments can cause improper leakage of sensitive information, in this paper we extend loose associations to operate over an arbitrary number of fragments. We first illustrate how the publication of multiple loose associations between different pairs of fragments can potentially expose sensitive associations, and describe an approach for defining loose associations among an arbitrary set of fragments. We investigate how tuples in fragments can be grouped for producing loose associations so to increase the utility of queries executed over fragments. We then provide a heuristics for performing such a grouping and producing loose associations satisfying a given level of protection for sensitive associations, while achieving utility for queries over different fragments. We also illustrate the result of an extensive experimental effort over both synthetic and real datasets, which shows the efficiency and the enhanced utility provided by our proposal.

Data outsourcing and cloud computing have been emerging at an ever-growing rate as successful approaches for allowing users and companies to rely on external services for storing and managing data. As data and access to them are not under the control of the data owner, there is a clear need to provide proper confidentiality protection. Such requirements concern the confidentiality not only of the stored data (content) but also of the specific accesses (or patterns of them) that users make on such data. In this article, we address these issues and propose an approach for guaranteeing content, access, and pattern confidentiality in a data outsourcing scenario. The proposed solution is based on the definition of a shuffle index structure, which adapts traditional B +-trees and, by applying a combination of techniques (covers, caches, and shuffling), ensures confidentiality of the data and of queries over them, protecting each single access as well as sequences thereof. The proposed solution also supports update operations over the data, while making reads and writes not recognizable as such by the server. We show that the shuffle index exhibits a limited performance cost, thus resulting effectively usable in practice.

Fragmentation has been recently proposed as a promising approach to protect the confidentiality of sensitive associations whenever data need to undergo external release or storage. By splitting attributes among different fragments, fragmentation guarantees confidentiality of the associations among these attributes under the assumption that such associations cannot be reconstructed by re-combining the fragments. We note that the requirement that fragments do not have attributes in common, imposed by previous proposals, is only a necessary, but not sufficient, condition to ensure that information in different fragments cannot be recombined as dependencies may exist among data enabling some form of linkability. In this paper, we identify the problem of improper information leakage due to data dependencies, provide a formulation of the problem based on a natural graphical modeling, and present an approach to tackle it in an efficient and scalable way.

The evolution of the Information and Communication Technology (ICT) has radically changed our lives, making information available from anywhere at any time through different kinds of devices. The advantage of the growing availability of computational power and connectivity resources at low prices is the easiness of collecting, sharing, processing, and accessing information. This advantage however does not come for free as it introduces unprecedented privacy risks. The attention toward these problems is growing every day and the need for privacy-aware policies, regulations, and techniques has been widely recognized. WPES is a yearly forum, this year at its 12th edition, that brings together researchers and practitioners interested in discussing the privacy issues characterizing our global and interconnected society.

Data outsourcing has recently emerged as a successful solution allowing individuals and organizations to delegate data and service management to external third parties. A major challenge in the data outsourcing scenario is how to guarantee proper privacy protection against the external server. Recent promising approaches rely on the organization of data in indexing structures that use encryption and the dynamic allocation of encrypted data to physical blocks for destroying the otherwise static relationship between data and the blocks in which they are stored. However, dynamic data allocation implies the need to re-write blocks at every read access, thus requesting exclusive locks that can affect concurrency. Also, these solutions only support search conditions on the values of the attribute used for building the indexing structure. In this paper, we present an approach that overcomes such limitations by extending the recently proposed shuffle index structure with support for concurrency and multiple indexes. Support for concurrency relies on the use of several differential versions of the data index that are periodically reconciled and applied to the main data structure. Support for multiple indexes relies on the definition of secondary shuffle indexes that are then combined with the primary index in a single data structure whose content and allocation is unintelligible to the server. We show how using such differential versions and combined index structure guarantees privacy, provides support for concurrent accesses and multiple search conditions, and considerably increases the performance of the system and the applicability of the proposed solution.

We address the problem of providing users with the ability to assess the integrity of join results produced by external computational providers and computed over externally stored databases. Our approach relies on different mutually supporting techniques offering strong integrity protection guarantees at a limited cost. The application of the approach is completely transparent to the computational provider, against which data and query confidentiality are preserved. The paper introduces our techniques analytically, examining their protection guarantees and performance. It also illustrates experimental results, which confirm the effectiveness and efficiency of our solutions.

Ensuring proper privacy and protection of the information stored, communicated, processed, and disseminated in the cloud as well as of the users accessing such an information is one of the grand challenges of our modern society. As a matter of fact, the advancements in the Information Technology and the diffusion of novel paradigms such as data outsourcing and cloud computing, while allowing users and companies to easily access high quality applications and services, introduce novel privacy risks of improper information disclosure and dissemination. In this paper, we will characterize different aspects of the privacy problem in emerging scenarios. We will illustrate risks, solutions, and open problems related to ensuring privacy of users accessing services or resources in the cloud, sensitive information stored at external parties, and accesses to such an information.

The proper protection of data privacy is a complex task that requires a careful analysis of what actually has to be kept private. Several definitions of privacy have been proposed over the years, from traditional syntactic privacy definitions, which capture the protection degree enjoyed by data respondents with a numerical value, to more recent semantic privacy definitions, which take into consideration the mechanism chosen for releasing the data. In this paper, we illustrate the evolution of the definitions of privacy, and we survey some data protection techniques devised for enforcing such definitions. We also illustrate some well-known application scenarios in which the discussed data protection techniques have been successfully used, and present some open issues.

Data sharing and dissemination are becoming increasingly important for conducting our daily life activities. The main consequence of this trend is that huge collections of data are easily available and accessible, leading to growing privacy concerns. The research community has devoted many efforts aiming at addressing the complex privacy requirements that characterize the modern Information Society. Although several advancements have been made, still many open issues need to be investigated. In this paper, we consider a scenario where data are incrementally released and we address the privacy problem arising when sensitive non released properties depend on (and can therefore be inferred from) non-sensitive released data. We propose a model capturing this inference problem, where sensitive information is characterized by peculiar value distributions of non sensitive released data. We then describe how to counteract possible inferences that an observer can draw by applying different statistical metrics on released data. Finally, we perform an experimental evaluation of our solution, showing its efficacy.

With the growing needs for data sharing and dissemination, privacy-preserving data publishing is becoming an important issue that still requires further investigation. In this paper, we make a step towards private data publication by proposing a solution based on the release of vertical views (frag ments) over a relational table that satisfy confidentiality and visibility constraints expressing requirements for information protection and release, respectively. We translate the problem of computing a fragmentation composed of the minimum number of fragments into the problem of computing a maximum weighted clique over a fragmentation graph. The fragmentation graph models fragments, efficiently computed using Ordered Binary Decision Diagrams (OBDDs), that satisfy all the confidentiality constraints and a subset of the visibility constraints defined in the system. We then show an exact and a heuristic algorithm for computing a minimal and a locally minimal fragmentation, respectively. Finally, we provide experimental results comparing the execution time and the fragmentations returned by the exact and heuristic algorithms. The experiments show that the heuristic algorithm has low computation cost and computes a fragmentation close to optimum.

The widespread diffusion of Web-based services provided by public and private organizations emphasizes the need for a flexible solution for protecting the information accessible through Web applications. A promising approach is represented by credential-based access control and trust management. However, although much research has been done and several proposals exist, a clear obstacle to the realization of their benefits in data-intensive Web applications is represented by the lack of adequate support in the DBMSs. As a matter of fact, DBMSs are often responsible for the management of most of the information that is accessed using a Web browser or a Web service invocation. In this article, we aim at eliminating this gap, and present an approach integrating trust management with the access control of the DBMS. We propose a trust model with a SQL syntax and illustrate an algorithm for the efficient verification of a delegation path for certificates. Our solution nicely complements current trust management proposals allowing the efficient realization of the services of an advanced trust management model within current relational DBMSs. An important benefit of our approach lies in its potential for a robust end-to-end design of security for personal data in Web scenario, where vulnerabilities of Web applications cannot be used to violate the protection of the data residing on the database server. We also illustrate the implementation of our approach within an open-source DBMS discussing design choices and performance impact.

As the use of external storage and data processing services for storing and managing sensitive data becomes more and more common, there is an increasing need for novel techniques that support not only data confidentiality, but also confidentiality of the accesses that users make on such data. In this paper, we propose a technique for guaranteeing content, access, and pattern confidentiality in the data outsourcing scenario. The proposed technique introduces a shuffle index structure, which adapts traditional B+-trees. We show that our solution exhibits a limited performance cost, thus resulting effectively usable in practice.

The International Conference on Network and System Security (NSS) is an annual conference on all theoretical and practical aspects related to information, network, and system security. The fifth International Conference on Network and System Security (NSS 2011) was held in Milan, Italy, September 6 to September 8, 2011.

Existing approaches for protecting sensitive information outsourced at external “honest-but-curious” servers are typically based on an overlying layer of encryption applied to the whole database, or on the combined use of fragmentation and encryption. In this paper, we put forward a novel paradigm for preserving privacy in data outsourcing, which departs from encryption. The basic idea is to involve the owner in storing a limited portion of the data, while storing the remaining information in the clear at the external server. We analyze the problem of computing a fragmentation that minimizes the owner's workload, which is represented using different metrics and corresponding weight functions, and prove that this minimization problem is NP-hard. We then introduce the definition of locally minimal fragmentation that is used to efficiently compute a fragmentation via a heuristic algorithm. The algorithm translates the problem of finding a locally minimal fragmentation in terms of a hypergraph 2-coloring problem. Finally, we illustrate the execution of queries on fragments and provide experimental results comparing the fragmentations returned by our heuristics with respect to optimal fragmentations. The experiments show that the heuristics guarantees a low computation cost and is able to compute a fragmentation close to optimum.

We present a simple, yet powerful, approach for the specification and enforcement of authorizations regulating data release among data holders collaborating in a distributed computation, to ensure that query processing discloses only data whose release has been explicitly authorized. Data disclosure is captured by means of profiles, associated with each data computation, that describe the information carried by a base or a derived (i.e., computed by a query) relation. We present an algorithm that, given a query plan, determines whether it can be safely executed and produces a safe execution strategy for it. For each operation in a safe query plan, the algorithm determines the server(s) responsible for the execution, based on the entailed information flows, considering different strategies for the execution of joins. Finally, we discuss the architecture of a distributed database system based on the proposed model, illustrating possible design choices and their impact.

We propose a modeling of the problem of privacy-compliant data publishing that captures confidentiality constraints on one side and visibility requirements on the other side. Confidentiality constraints express the fact that some attributes, or associations among them, are sensitive and cannot be released. Visibility requirements express requests for views over data that should be provided. We propose a solution based on data fragmentation to split sensitive associations while ensuring visibility. In addition, we show how sensitive associations broken by fragmentation can be released in a sanitized form as loose associations formed in a way to guarantee a specified degree of privacy.

The impact of privacy requirements in the development of modern applications is increasing very quickly. Many commercial and legal regulations are driving the need to develop reliable solutions for protecting sensitive information whenever it is stored, processed, or communicated to external parties. To this purpose, encryption techniques are currently used in many scenarios where data protection is required since they provide a layer of protection against the disclosure of personal information, which safeguards companies from the costs that may arise from exposing their data to privacy breaches. However, dealing with encrypted data may make query processing more expensive. In this article, we address these issues by proposing a solution to enforce the privacy of data collections that combines data fragmentation with encryption. We model privacy requirements as confidentiality constraints expressing the sensitivity of attributes and their associations. We then use encryption as an underlying (conveniently available) measure for making data unintelligible while exploiting fragmentation as a way to break sensitive associations among attributes. We formalize the problem of minimizing the impact of fragmentation in terms of number of fragments and their affinity and present two heuristic algorithms for solving such problems. We also discuss experimental results, comparing the solutions returned by our heuristics with respect to optimal solutions, which show that the heuristics, while guaranteeing a polynomial-time computation cost are able to retrieve solutions close to optimum.

Current access control models typically assume that resources are under the strict custody of a trusted party which monitors each access request to verify if it is compliant with the specified access control policy. There are many scenarios where this approach is becoming no longer adequate. Many clear trends in Web technology are creating a need for owners of sensitive information to manage access to it by legitimate users using the services of honest but curious third parties, that is, parties trusted with providing the required service but not authorized to read the actual data content. In this scenario, the data owner encrypts the data before outsourcing and stores them at the server. Only the data owner and users with knowledge of the key will be able to decrypt the data. Possible access authorizations are to be enforced by the owner. In this article, we address the problem of enforcing selective access on outsourced data without need of involving the owner in the access control process. The solution puts forward a novel approach that combines cryptography with authorizations, thus enforcing access control via selective encryption . The article presents a formal model for access control management and illustrates how an authorization policy can be translated into an equivalent encryption policy while minimizing the amount of keys and cryptographic tokens to be managed. The article also introduces a two-layer encryption approach that allows the data owner to outsource, besides the data, the complete management of the authorization policy itself, thus providing efficiency and scalability in dealing with policy updates. We also discuss experimental results showing that our approach is able to efficiently manage complex scenarios.

The balance between privacy and utility is a classical problem with an increasing impact on the design of modern information systems. On the one side it is crucial to ensure that sensitive information is properly protected; on the other side, the impact of protection on the workload must be limited as query efficiency and system performance remain a primary requirement. We address this privacy/efficiency balance proposing an approach that, starting from a flexible definition of confidentiality constraints on a relational schema, applies encryption on information in a parsimonious way and mostly relies on fragmentation to protect sensitive associations among attributes. Fragmentation is guided by workload considerations so to minimize the cost of executing queries over fragments. We discuss the minimization problem when fragmenting data and provide a heuristic approach to its solution.

We propose an approach for the selective enforcement of access control restrictions in, possibly distributed, large data collections based on two basic concepts: i) flexible authorizations identify, in a declarative way, the data that can be released, and ii) queries are checked for execution not with respect to individual authorizations but rather evaluating whether the information release they (directly or indirectly) entail is allowed by the authorizations. Our solution is based on the definition of query profiles capturing the information content of a query and builds on a graph-based modeling of database schema, authorizations, and queries. Access control is then effectively modeled and efficiently executed in terms of graph coloring and composition and on traversal of graph paths. We then provide a polynomial composition algorithm for determining if a query is authorized.

We present a simple, yet powerful, approach for the specification and enforcement of authorizations regulating data release among data holders collaborating in a distributed computation, to ensure that query processing discloses only data whose release has been explicitly authorized. Data disclosure is captured by means of profiles, associated with each data computation, that describe the information carried by the result. We also present an algorithm that, given a query plan, determines whether it can be safely executed and produces a safe execution strategy. The main advantage of our approach is its simplicity that, without impacting expressiveness, makes it nicely interoperable with current solutions for collaborative computations in distributed database systems.