Biblio

In recent years, many users have uploaded data to the cloud for easy storage and sharing with other users. At the same time, security and privacy concerns for the data are growing. Attribute-based encryption (ABE) enables both data security and access control by defining users with attributes so that only those users who have matching attributes can decrypt them. For real-world applications of ABE, revocation of users or their attributes is necessary so that revoked users can no longer decrypt the data. In actual implementations, ABE is used in hybrid with a symmetric encryption scheme such as the advanced encryption standard (AES) where data is encrypted with AES and the AES key is encrypted with ABE. The hybrid encryption scheme requires re-encryption of the data upon revocation to ensure that the revoked users can no longer decrypt that data. To re-encrypt the data, the data owner (DO) must download the data from the cloud, then decrypt, encrypt, and upload the data back to the cloud, resulting in both huge communication costs and computational burden on the DO depending on the size of the data to be re-encrypted. In this paper, we propose an attribute-based proxy re-encryption method in which data can be re-encrypted in the cloud without downloading any data by adopting both ABE and Syalim's encryption scheme. Our proposed scheme reduces the communication cost between the DO and cloud storage. Experimental results show that the proposed method reduces the communication cost by as much as one quarter compared to that of the trivial solution.

Attributes-based encryption (ABE) is a promising cryptographic mechanism that provides a fine-grained access control for cloud environment. Since most of the parties exchange sensitive data among them by using cloud computing, data protection is very important for data confidentiality. Ciphertext policy attributes-based encryption (CP-ABE) is one of the ABE schemes, which performs an access control of security mechanisms for data protection in cloud storage. In CP-ABE, each user has a set of attributes and data encryption is associated with an access policy. The secret key of a user and the ciphertext are dependent upon attributes. A user is able to decrypt a ciphertext if and only if his attributes satisfy the access structure in the ciphertext. The practical applications of CP-ABE have still requirements for attributes policy management and user revocation. This paper proposed an important issue of policy revocation in CP-ABE scheme. In this paper, sensitive parts of personal health records (PHRs) are encrypted with the help of CP-ABE. In addition, policy revocation is considered to add in CP-ABE and generates a new secret key for authorized users. In proposed attributes based encryption scheme, PHRs owner changes attributes policy to update authorized user lists. When policy revocation occurs in proposed PHRs sharing system, a trusted authority (TA) calculates a partial secret token key according to a policy updating level and then issues new or updated secret keys for new policy. Proposed scheme emphasizes on key management, policy management and user revocation. It provides a full control on data owner according to a policy updating level what he chooses. It helps both PHRs owner and users for flexible policy revocation in CP-ABE without time consuming.

Outsourcing the decryption of attribute-based encryption (ABE) ciphertext is a promising way to tackle the question of how users can perform decryption efficiently. However, existing solutions require the type of the target ciphertext to be determined at the setup of the outsourcing scheme. As such, making the target cryptosystems (or the clients) to be versatile becomes an issue that warrants investigations. In this paper, the problem we wish to tackle is to transform an ABE ciphertext to any client who is using the same, or possibly different, public-key encryption (PKE) system with the sender. The problem is of practical interest since it is hard to require all clients to use the same PKE, especially in the case of remote and cross-system data sharing. In addition, we also consider whether robust client-side decryption scheme can be adopted. This feature is not supported in the existing ABE with outsourcing. We introduce cross-system proxy re-encryptions (CS-PRE), a new re-encryption paradigm in which a semi-trusted proxy converts a ciphertext of a source cryptosystem (\$\textparagraphi\_0\$) into a ciphertext for a target cryptosystem (\$\textparagraphi\$). We formalize CS-PRE and present a construction that performs well in the following aspects. (1)Versatility: \$\textparagraphi\_0\$ can be any attribute-based encryption (ABE) within Attrapadung's pair encoding framework. \$\textparagraphi\$ can be any public-key encryption. Furthermore, the keys and public parameters can be generated independently. (2) Compatibility: CS-PRE does not modify the public parameters and keys of \$\textparagraphi\_0\$ and \$\textparagraphi\$. Besides, input for the conversion is an ordinary ciphertext of \$\textparagraphi\_0\$. (3) Efficiency: The computational cost for re-encryption and decryption of the re-encrypted ciphertext are roughly the same as a decryption in \$\textparagraphi\_0\$ and \$\textparagraphi\$ respectively. We prove that our construction is fully secure assuming \$\textparagraphi\_0\$ is secure in Attrapadung's framework and \$\textparagraphi\$ is IND-CPA secure. Furthermore, it remains secure when there are multiple target cryptosystems. As with other proxy re-encryption, CS-PRE enables flexible sharing of cloud data, as the owner can instruct the cloud server to re-encrypt his ciphertext to those for the intended recipient. In addition, it allows lightweight devices to enjoy access to remote data encrypted under powerful but possibly costly encryption, such as functional encryption, by utilizing the server's power in converting the ciphertext to a simpler encryption, such as RSA. Finally, instances of CS-PRE can be viewed as new proxy re-encryption schemes, such as a PRE supporting ABE for regular language to Hierarchical IBE or Doubly Spatial Encryption to lattice-based encryptions (e.g. NTRUCCA).

In contrast to the Electronic Medical Record (EMR) and Electronic Health Record (EHR) systems that are created to maintain and manage patient data by health professionals and organizations, Personal Health Record (PHR) systems are operated and managed by patients. Therefore, it necessitates increased attention to the importance of security and privacy challenges, as patients are most often unfamiliar with the potential security threats that can result from release of their health data. On the other hand, the use of PHR systems is increasingly becoming an important part of the healthcare system by sharing patient information among their circle of care. To have a system with a more favorable interface and a high level of security, it is crucial to provide a mobile application for PHR that fulfills six important features: (1) ease the usage for various patient demographics and their delegates, (2) security, (3) quickly transfer patient data to their health professionals, (4) give the ability of access revocation to the patient, (5) provide ease of interaction between patients and their circle of care, and (6) inform patients about any instances of access to their data by their circle of care. In this work, we propose an implementation of a Privacy-Preserving PHR system (P3HR) for Android devices to fulfill the above six characteristics, using a Ciphertext Policy Attribute Based Encryption to enhance security and privacy of the system, as well as providing access revocation in a hierarchical scheme of the health professionals and organizations involved. Using this application, patients can securely store their health data, share the records, and receive feedback and recommendations from their circle of care.

Recently, Jung et al. [1] proposed a data access privilege scheme and claimed that their scheme addresses data and identity privacy as well as multi-authority, and provides data access privilege for attribute-based encryption. In this paper, we show that this scheme, and also its former and latest versions (i.e. [2] and [3] respectively) suffer from a number of weaknesses in terms of finegrained access control, users and authorities collusion attack, user authorization, and user anonymity protection. We then propose our new scheme that overcomes these shortcomings. We also prove the security of our scheme against user collusion attacks, authority collusion attacks and chosen plaintext attacks. Lastly, we show that the efficiency of our scheme is comparable with existing related schemes.

It is increasingly common to outsource data storage to untrusted, third party (e.g. cloud) servers. However, in such settings, low-level online reference monitors may not be appropriate for enforcing read access, and thus cryptographic enforcement schemes (CESs) may be required. Much of the research on cryptographic access control has focused on the use of specific primitives and, primarily, on how to generate appropriate keys and fails to model the access control system as a whole. Recent work in the context of role-based access control has shown a gap between theoretical policy specification and computationally secure implementations of access control policies, potentially leading to insecure implementations. Without a formal model, it is hard to (i) reason about the correctness and security of a CES, and (ii) show that the security properties of a particular cryptographic primitive are sufficient to guarantee security of the CES as a whole. In this paper, we provide a rigorous definitional framework for a CES that enforces read-only information flow policies (which encompass many practical forms of access control, including role-based policies). This framework (i) provides a tool by which instantiations of CESs can be proven correct and secure, (ii) is independent of any particular cryptographic primitives used to instantiate a CES, and (iii) helps to identify the limitations of current primitives (e.g. key assignment schemes) as components of a CES.

User attribution process based on human inherent dynamics and preference is one area of research that is capable of elucidating and capturing human dynamics on the Internet. Prior works on user attribution concentrated on behavioral biometrics, 1-to-1 user identification process without consideration for individual preference and human inherent temporal tendencies, which is capable of providing a discriminatory baseline for online users, as well as providing a higher level classification framework for novel user attribution. To address these limitations, the study developed a temporal model, which comprises the human Polyphasia tendency based on Polychronic-Monochronic tendency scale measurement instrument and the extraction of unique human-centric features from server-side network traffic of 48 active users. Several machine-learning algorithms were applied to observe distinct pattern among the classes of the Polyphasia tendency, through which a logistic model tree was observed to provide higher classification accuracy for a 1-to-N user attribution process. The study further developed a high-level attribution model for higher-level user attribution process. The result from this study is relevant in online profiling process, forensic identification and profiling process, e-learning profiling process as well as in social network profiling process.

NoSQL databases have gained a lot of popularity over the last few years. They are now used in many new system implementations that work with vast amounts of data. This data will typically also include sensitive information that needs to be secured. NoSQL databases are also underlying a number of cloud implementations which are increasingly being used to store sensitive information by various organisations. This has made NoSQL databases a new target for hackers and other state sponsored actors. Forensic examinations of compromised systems will need to be conducted to determine what exactly transpired and who was responsible. This paper examines specifically if NoSQL databases have security features that leave relevant traces so that accurate forensic attribution can be conducted. The seeming lack of default security measures such as access control and logging has prompted this examination. A survey into the top ranked NoSQL databases was conducted to establish what authentication and authorisation features are available. Additionally the provided logging mechanisms were also examined since access control without any auditing would not aid forensic attribution tremendously. Some of the surveyed NoSQL databases do not provide adequate access control mechanisms and logging features that leave relevant traces to allow forensic attribution to be done using those. The other surveyed NoSQL databases did provide adequate mechanisms and logging traces for forensic attribution, but they are not enabled or configured by default. This means that in many cases they might not be available, leading to insufficient information to perform accurate forensic attribution even on those databases.

As the development of technology increases, the security risk also increases. This has affected most organizations, irrespective of size, as they depend on the increasingly pervasive technology to perform their daily tasks. However, the dependency on technology has introduced diverse security vulnerabilities in organizations which requires a reliable preparedness for probable forensic investigation of the unauthorized incident. Keystroke dynamics is one of the cost-effective methods for collecting potential digital evidence. This paper presents a keystroke pattern analysis technique suitable for the collection of complementary potential digital evidence for forensic readiness. The proposition introduced a technique that relies on the extraction of reliable behavioral signature from user activity. Experimental validation of the proposition demonstrates the effectiveness of proposition using a multi-scheme classifier. The overall goal is to have forensically sound and admissible keystroke evidence that could be presented during the forensic investigation to minimize the costs and time of the investigation.

Whilst the fundamental composition of digital forensic readiness have been expounded by myriad literature, the integration of behavioral modalities have not been considered. Behavioral modalities such as keystroke and mouse dynamics are key components of human behavior that have been widely used in complementing security in an organization. However, these modalities present better forensic properties, thus more relevant in investigation/incident response, than its deployment in security. This study, therefore, proposes a forensic framework which encompasses a step-by-step guide on how to integrate behavioral biometrics into digital forensic readiness process. The proposed framework, behavioral biometrics-based digital forensics readiness framework (BBDFRF) comprised four phases which include data acquisition, preservation, user-authentication, and user pattern attribution phase. The proposed BBDFRF is evaluated in line with the ISO/IEC 27043 standard for proactive forensics, to address the gap on the integration of the behavioral biometrics into proactive forensics. BBDFRF thus extends the body of literature on the forensic capability of behavioral biometrics. The implementation of this framework can be used to also strengthen the security mechanism of an organization, particularly on continuous authentication.

Extensible language frameworks aim to allow independently-developed language extensions to be easily added to a host programming language. It should not require being a compiler expert, and the resulting compiler should "just work" as expected. Previous work has shown how specifications for parsing (based on context free grammars) and for semantic analysis (based on attribute grammars) can be automatically and reliably composed, ensuring that the resulting compiler does not terminate abnormally. However, this work does not ensure that a property proven to hold for a language (or extended language) still holds when another extension is added, a problem we call interference. We present a solution to this problem using of a logical notion of coherence. We show that a useful class of language extensions, implemented as attribute grammars, preserve all coherent properties. If we also restrict extensions to only making use of coherent properties in establishing their correctness, then the correctness properties of each extension will hold when composed with other extensions. As a result, there can be no interference: each extension behaves as specified.

Access control offers mechanisms to control and limit the actions or operations that are performed by a user on a set of resources in a system. Many access control models exist that are able to support this basic requirement. One of the properties examined in the context of these models is their ability to successfully restrict access to resources. Nevertheless, considering only restriction of access may not be enough in some environments, as in critical infrastructures. The protection of systems in this type of environment requires a new line of enquiry. It is essential to ensure that appropriate access is always possible, even when users and resources are subjected to challenges of various sorts. Resilience in access control is conceived as the ability of a system not to restrict but rather to ensure access to resources. In order to demonstrate the application of resilience in access control, we formally define an attribute based access control model (ABAC) based on guidelines provided by the National Institute of Standards and Technology (NIST). We examine how ABAC-based resilience policies can be specified in temporal logic and how these can be formally verified. The verification of resilience is done using an automated model checking technique, which eventually may lead to reducing the overall complexity required for the verification of resilience policies and serve as a valuable tool for administrators.

In this talk, I will discuss my lab's work in the emerging field of adversarial stylometry and machine learning. Machine learning algorithms are increasingly being used in security and privacy domains, in areas that go beyond intrusion or spam detection. For example, in digital forensics, questions often arise about the authors of documents: their identity, demographic background, and whether they can be linked to other documents. The field of stylometry uses linguistic features and machine learning techniques to answer these questions. We have applied stylometry to difficult domains such as underground hacker forums, open source projects (code), and tweets. I will discuss our Doppelgnger Finder algorithm, which enables us to group Sybil accounts on underground forums and detect blogs from Twitter feeds and reddit comments. In addition, I will discuss our work attributing unknown source code and binaries.

Attribute-Based Access Control (ABAC) has received significant attention in recent years, although the concept has been around for over two decades now. Many ABAC models, with different variations, have been proposed and formalized. Besides basic ABAC models, there are models designed with additional capabilities such as group attributes, group and attribute hierarchies and so on. Hierarchical relationship among groups and attributes enhances access control flexibility and facilitates attribute management and administration. However, implementation and demonstration of ABAC models in real-world applications is still lacking. In this paper, we present a restricted HGABAC (rHGABAC) model with user and object groups and group hierarchy. We then introduce attribute hierarchies in this model. We also present an authorization architecture for implementing rHGABAC utilizing the NIST Policy Machine (PM). PM allows to define attribute-based access control policies, however, the attributes in PM are different in nature than attributes in typical ABAC models as name-value pairs. We identify a policy configuration mechanism for our proposed model employing PM capabilities, and demonstrate use cases and their configuration and implementation in PM using our authorization architecture.

In this paper, we introduce the concept of transforming attribute-value assignments from one set to another set. We specify two types of transformations–-attribute reduction and attribute expansion. We distinguish policy attributes from non-policy attributes in that policy attributes are used in authorization policies whereas the latter are not. Attribute reduction is a process of contracting a large set of assignments of non-policy attributes into a possibly smaller set of policy attribute-value assignments. This process is useful for abstracting attributes that are too specific for particular types of objects or users, designing modular authorization policies, and modeling hierarchical policies. On the other hand, attribute expansion is a process of performing a large set of attribute-value assignments to users or objects from a possibly smaller set of assignments. We define a language for specifying mapping for the transformation process. We also identify and discuss various issues that stem from the transformation process.

Some online communities are better than others in standardizing and automating the attribution process. This study examines how automated attribution can alleviate attribution apprehension and thus facilitate creative integration in open communities. Attribution apprehension, i.e., a user's anxiety over proper attribution of reused artifacts, adversely impacts the tendencies to engage in the integration process. Because open communities thrive on the basis of fairness, automated attribution features are essential in fostering creative integration. This study draws upon task-technology fit to craft a theoretical framework for explaining this phenomenon, reviews current tools for automated attribution in different communities and describes findings of a pilot survey on how those tools can encourage creative integration.

Access control is one of the most challenging issues in Cloud environment, it must ensure data confidentiality through enforced and flexible access policies. The revocation is an important task of the access control process, generally it consists on banishing some roles from the users. Attribute-based encryption is a promising cryptographic method which provides the fine-grained access, which makes it very useful in case of group sharing applications. This solution has initially been developed on a central authority model. Later, it has been extended to a multi-authority model which is more convenient and more reliable. However, the revocation problem is still the major challenge of this approach. There have been few proposed revocation solutions for the Multi-authority scheme and these solutions suffer from the lack of efficiency. In this paper, we propose an access control mechanism on a multi-authority architecture with an immediate and efficient attributes' or users' revocation. The proposed scheme uses decentralized CP-ABE to provide flexible and fine-grained access. Our solution provides collusion resistance, prevents security degradations, supports scalability and does not require keys' redistribution.

In cloud computing, computationally weak users are always willing to outsource costly computations to a cloud, and at the same time they need to check the correctness of the result provided by the cloud. Such activities motivate the occurrence of verifiable computation (VC). Recently, Parno, Raykova and Vaikuntanathan showed any VC protocol can be constructed from an attribute-based encryption (ABE) scheme for a same class of functions. In this paper, we propose two practical and efficient semi-adaptively secure key-policy attribute-based encryption (KP-ABE) schemes with constant-size ciphertexts. The semi-adaptive security requires that the adversary designates the challenge attribute set after it receives public parameters but before it issues any secret key query, which is stronger than selective security guarantee. Our first construction deals with small universe while the second one supports large universe. Both constructions employ the technique underlying the prime-order instantiation of nested dual system groups, which are based on the \$d\$-linear assumption including SXDH and DLIN assumptions. In order to evaluate the performance, we implement our ABE schemes using \$\textbackslashtextsf\Python\\$ language in Charm. Compared with previous KP-ABE schemes with constant-size ciphertexts, our constructions achieve shorter ciphertext and secret key sizes, and require low computation costs, especially under the SXDH assumption.

Secure Data Sharing (SDS) enables users to share data in the cloud in a confidential and integrity-preserving manner. Many recent SDS approaches are based on Attribute-Based Encryption (ABE), leveraging the advantage that ABE allows to address a multitude of users with only one ciphertext. However, ABE approaches often come with the downside that they require a central fully-trusted entity that is able to decrypt any ciphertext in the system. In this paper, we investigate on whether ABE could be used to efficiently implement Decentralized Secure Data Sharing (D-SDS), which explicitly demands that the authorization and access control enforcement is carried out solely by the owner of the data, without the help of a fully-trusted third party. For this purpose, we did a comprehensive analysis of recent ABE approaches with regard to D-SDS requirements. We found one ABE approach to be suitable, and we show different alternatives to employ this ABE approach in a group-based D-SDS scenario. For a realistic estimation of the resource consumption, we give concrete resource consumption values for workloads taken from real-world system traces and exemplary up-to-date mobile devices. Our results indicate that for the most D-SDS operations, the resulting computation times and outgoing network traffic will be acceptable in many use cases. However, the computation times and outgoing traffic for the management of large groups might prevent using mobile devices.

In cloud computing, computationally weak users are always willing to outsource costly computations to a cloud, and at the same time they need to check the correctness of the result provided by the cloud. Such activities motivate the occurrence of verifiable computation (VC). Recently, Parno, Raykova and Vaikuntanathan showed any VC protocol can be constructed from an attribute-based encryption (ABE) scheme for a same class of functions. In this paper, we propose two practical and efficient semi-adaptively secure key-policy attribute-based encryption (KP-ABE) schemes with constant-size ciphertexts. The semi-adaptive security requires that the adversary designates the challenge attribute set after it receives public parameters but before it issues any secret key query, which is stronger than selective security guarantee. Our first construction deals with small universe while the second one supports large universe. Both constructions employ the technique underlying the prime-order instantiation of nested dual system groups, which are based on the \$d\$-linear assumption including SXDH and DLIN assumptions. In order to evaluate the performance, we implement our ABE schemes using \$\textbackslashtextsf\Python\\$ language in Charm. Compared with previous KP-ABE schemes with constant-size ciphertexts, our constructions achieve shorter ciphertext and secret key sizes, and require low computation costs, especially under the SXDH assumption.

Two mainstream techniques are traditionally used to authorize access to a WiFi network. Small scale networks usually rely on the offline distribution of a WPA/WPA2 static pre-shared secret key (PSK); security hence relies on the fact that this PSK is not leaked by end user, and is not disclosed via dictionary or brute-force attacks. On the other side, Enterprise and large scale networks typically employ online authorization using an 802.1X-based authentication service leveraging a backend online infrastructure (e.g. Radius servers/proxies). In this work, we propose a new mechanism which does not require neither online operation nor backend access control infrastructure, but which does not force us to rely on a static pre-shared secret key. The idea is very simple, yet effective: directly broadcast in the WLAN beacons an encrypted version of the secret key required to access the WLAN network, so that only the users which possess suitable authorization credentials can decrypt and use it. This proposed approach clearly decouples the management of authorization credentials, issued offline to the authorized end users, from the actual secret key used in the WLAN network, which can thus be in principle changed at each new user's access. The solution described in the paper relies on attribute-based encryption, and is designed to be compatible with WPA2 and deployable within standard 802.11 management frames. Since no user identification is required (access control is based on attributes rather than on the user identity), the proposed approach further improves privacy. We demonstrate the feasibility of the proposed solution via a concrete implementation in Linux-based devices and via relevant testing in a real-world experimental setup.

The following decade will witness a surge in remote health-monitoring systems that are based on body-worn monitoring devices. These Medical Cyber Physical Systems (MCPS) will be capable of transmitting the acquired data to a private or public cloud for storage and processing. Machine learning algorithms running in the cloud and processing this data can provide decision support to healthcare professionals. There is no doubt that the security and privacy of the medical data is one of the most important concerns in designing an MCPS. In this paper, we depict the general architecture of an MCPS consisting of four layers: data acquisition, data aggregation, cloud processing, and action. Due to the differences in hardware and communication capabilities of each layer, different encryption schemes must be used to guarantee data privacy within that layer. We survey conventional and emerging encryption schemes based on their ability to provide secure storage, data sharing, and secure computation. Our detailed experimental evaluation of each scheme shows that while the emerging encryption schemes enable exciting new features such as secure sharing and secure computation, they introduce several orders-of-magnitude computational and storage overhead. We conclude our paper by outlining future research directions to improve the usability of the emerging encryption schemes in an MCPS.

Due to the high volume and velocity of big data, it is an effective option to store big data in the cloud, because the cloud has capabilities of storing big data and processing high volume of user access requests. Attribute-Based Encryption (ABE) is a promising technique to ensure the end-to-end security of big data in the cloud. However, the policy updating has always been a challenging issue when ABE is used to construct access control schemes. A trivial implementation is to let data owners retrieve the data and re-encrypt it under the new access policy, and then send it back to the cloud. This method incurs a high communication overhead and heavy computation burden on data owners. In this paper, we propose a novel scheme that enabling efficient access control with dynamic policy updating for big data in the cloud. We focus on developing an outsourced policy updating method for ABE systems. Our method can avoid the transmission of encrypted data and minimize the computation work of data owners, by making use of the previously encrypted data with old access policies. Moreover, we also design policy updating algorithms for different types of access policies. The analysis show that our scheme is correct, complete, secure and efficient.