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In privacy-preserving cross-device federated learning, users train a global model on their local data and submit encrypted local models, while an untrusted central server aggregates the encrypted models to obtain an updated global model. Prior work has demonstrated how to verify the correctness of aggregation in such a setting. However, such verification relies on strong assumptions, such as a trusted setup among all users under unreliable network conditions, or it suffers from expensive cryptographic operations, such as bilinear pairing. In this paper, we scrutinize the verification mechanism of prior work and propose a model recovery attack, demonstrating that most local models can be leaked within a reasonable time (e.g., 98% of encrypted local models are recovered within 21 h). Then, we propose VerSA, a verifiable secure aggregation protocol for cross-device federated learning. VerSA does not require any trusted setup for verification between users while minimizing the verification cost by enabling both the central server and users to utilize only a lightweight pseudorandom generator to prove and verify the correctness of model aggregation. We experimentally confirm the efficiency of VerSA under diverse datasets, demonstrating that \textsc{VerSA} is orders of magnitude faster than verification in prior work.

Public auditing enables efficient integrity checks of data assigned to cloud servers. In this paper, we revisit the public auditing for encrypted data, in which a major concern is how to effectively support data dynamics, i.e., data modification, insertion, and deletion. We first determine which factor in existing auditing schemes most limits data dynamics from a cost perspective. We then propose a novel public auditing scheme that provides data dynamics that are orders of magnitude faster than previous methods. Our novel auditing challenge-response protocol reduces the computation cost of the TPA significantly, thus increasing the verification speed for the auditing results. Performance and security analysis demonstrates that the proposed scheme generates minimal computation costs while guaranteeing data integrity and privacy against an untrusted cloud.

Cloud-based Internet of Things (IoT) management services are a promising means of ingesting data from globally dispersed devices. In this setting, it is important to regulate access to data managed by potentially untrusted cloud servers. Attribute-based encryption (ABE) is a highly effective tool for access control. However, applying ABE to IoT environments shows limitations in the following three aspects: First, the demands for storage resources increase in proportion to the complexity of the access control policies. Second, the computation cost of ABE is onerous for resource-limited devices. Lastly, ABE alone is intractable to prevent illegal key-sharing which leads to unauthorized access to data. In this paper, we propose an efficient and secure cloud-based IoT data management scheme using ABE. First, we remove the storage-side dependency on the complexity of the access control policies. Second, a substantial part of computationally intensive operations is securely outsourced to the cloud servers. Lastly, unauthorized access to data via illegal key-sharing is strictly forbidden. Our security analysis and experimental results show the security and practicability of the proposed scheme.

In this paper, we propose a scheme of quantum operation teleportation (QOT) utilizing local operations and five-qubit entangled state to achieve mutual authentication and key agreement for two clients in different realms. On the one hand, the scheme not only has the characteristics of the arbitrariness of the relevant operation, the certainty of sharing success and the constancy of entangled resources, but also realizes the mutual authentication among the four parties, ensuring the reliability and security of the task. On the other hand, considering the complexity of the operation, we complete the current QOT task as a whole, so the operation difficulty is low and relatively simple. In summary, our analysis is completely feasible under the existing technical conditions and this proposed scheme has practical significance.

HyperText Transfer Protocol (HTTP) cookies are widely used on the web to enhance communication efficiency between a client and a server by storing stateful information. However, cookies may contain private and sensitive information about users. Thus, in order to guarantee the security of cookies, most web browsers and servers support not only Transport Layer Security (TLS) but also other mechanisms such as HTTP Strict Transport Security and cookie flags. However, a recent study has shown that it is possible to circumvent cookie flags in HTTPS by exploiting a vulnerability in HTTP software that allows message truncation. In this paper, we propose a novel cookie hijacking attack called $rotten~cookie$ which deactivates cookie flags even if they are protected by TLS by exploiting a weakness in HTTP in terms of integrity checks. According to our investigation, all major browsers ignore uninterpretable sections of the header of HTTP response messages and accept incorrect formats without any rejection. We demonstrate that, when combined with TLS or application vulnerabilities, this form of attack can obtain private cookies by removing cookie flags. Thus, the attacker can impersonate a legitimate user in the eyes of the server when cookies are used as an authentication token. We prove the practicality of our attack by demonstrating that our attack can lead five major web browsers to accept a cookie without any cookie flags. We thus present a mitigation strategy for the transport layer to preserve cookie security against our attack.

Named Data Networking (NDN) is a new paradigm for the future Internet infrastructure based on routable named data. The NDN infrastructure consists of a new component called strategy layer. The strategy layer allows for dynamically selecting network interfaces taking into account network conditions such as delay to forward Interest messages toward a provider. However, defining proper criteria for selecting the best possible paths to forward Interest messages is challenging in this network because different parameters and conditions conflict one another when choosing the best interfaces. Moreover, in NDN, data can be retrieved from different sources. However, to the best of our knowledge, the previous forwarding strategy methods that can estimate from which path the valid data can be fetched have not considered an attacker who tries to inject fake data with the same name as valid data. Therefore, in this paper, we take a holistic, adaptive forwarding approach that takes into account various metrics: bandwidth, load, delay, and reliability. Especially, we propose a reliability metric that defines which path is more stable and reliable to retrieve legitimate data. Our evaluation demonstrates that the proposed method enables reliable message delivery against potential attackers that inject invalid data, in addition, our method introduces marginal delay compared with the conventional forwarding methods in NDN.

Deep learning services are now deployed in various fields on top of cloud infrastructures. In such cloud environment, virtualization technology provides logically independent and isolated computing space for each tenant. However, recent studies demonstrate that by leveraging vulnerabilities of virtualization techniques and shared processor architectures in the cloud system, various side-channels can be established between cloud tenants. In this paper, we propose a novel attack scenario that can steal internal information of deep learning models by exploiting the Meltdown vulnerability in a multitenant system environment. On the basis of our experiment, the proposed attack method could extract internal information of a TensorFlow deep learning service with 92.875% accuracy and 1.325kB/s extraction speed.

Transport Layer Security (TLS) protocol is often vulnerable to version downgrade attacks, where a man-in-the-middle attacker interferes with the handshake protocol and leads the communicating parties to fall back from a higher version of TLS to lower ones, which are typically provided for backward compatibility. In order to thwart the downgrade attack, several defense mechanisms are adopted in most of the recent TLS versions. However, there have not been many studies on analyzing what conditions are needed to guarantee the theoretical security, and understanding how they are implemented in practice in the era of TLS 1.3. To understand the current deployment of downgrade protection mechanisms and their security in the real world, in this paper, we investigated ten major web browsers in five operating systems with diverse implementation conditions of TLS clients and servers. As a result, we identified that two network stacks of Microsoft and Apple are vulnerable to downgrade attacks. We then demonstrate TLS sessions can be downgraded from TLS 1.3 to 1.0 by exploiting the vulnerability. Drawing on our experiment, we analyze the root cause for the vulnerability, and present several mitigation strategies.

Searchable encryption (SE) schemes enable users to search over encrypted data that are outsourced to untrusted cloud servers. Although many software-based SE schemes have been developed to improve security and functionality, most of them have limitations in terms of performance and security assumptions. In order to increase the efficiency, and guarantee query and data privacy, several SE schemes based on trusted execution environment (TEE) such as Intel SGX, ARM TrustZone have been proposed recently. In this paper, we investigate the existing SGX-based searchable encryption schemes, and conduct a comparative analysis of them.

The Internet of Things (IoT) features a mechanism that extends connectivity to diverse computing devices, such as smart phones, commodity sensors, and appliances. Due to the huge quantity of data generated by the IoT devices, they are likely to be stored and managed by the cloud these days. However, because of the privacy concern about the sensitive data, encryption techniques are typically adopted by the cloud. In order to enable searching over encrypted data for multiple data senders in the cloud, public key encryption with keyword search (PEKS) has been proposed as one variant of searchable encryption (SE). Unfortunately, existing PEKS schemes are vulnerable to adaptive file-injection attack due to the lack of forward privacy. In this paper, we propose a forward secure PEKS scheme based on hierarchical identity-based encryption for cloud-assisted IoT environments. While the existing schemes incur to the data receiver a storage overhead that increases linearly with the number of data senders, our scheme incurs only a constant cost. The experimental analysis with Amazon EC2 and Raspberri Pi shows that our scheme is two to five times more efficient than the previous schemes, which makes our scheme more suitable for multiple data senders in the cloud-assisted IoT environments.

Network function virtualization takes advantage of virtualization technology to achieve flexibility in network service provisioning. However, it comes at the cost of security risks caused by cache side-channel attacks on virtual machines. In this study, we investigate the security impact of these attacks on virtualized network functions. In particular, we propose a novel cache-based reconnaissance technique against virtualized Linux-based firewalls. The proposed technique has significant advantages in the perspective of attackers. First, it enhances evasiveness against intrusion detection owing to the ability of source spoofing. Second, it allows inference on a wide variety of filtering rules. During experiment in VyOS, the proposed method could infer the firewall rules with an accuracy of more than 90% by using only a few dozen packets. We also present countermeasures to mitigate cache-based attacks on virtualized network functions.

Mail user agent (MUA) programs provide an integrated interface for email services. Many MUAs support email encryption functionality to ensure the confidentiality of emails. In practice, they encrypt the content of an email using email encryption standards such as OpenPGP or S/MIME, mostly implemented using GnuPG. Despite their widespread deployment, there has been insufficient research on their software structure and the security dependencies among the software components of MUA programs. In order to understand the security implications of the structures and analyze any possible vulnerabilities of MUA programs, we investigated a number of MUAs that support email encryption. As a result, we found severe vulnerabilities in a number of MUAs that allow cache side-channel attacks in virtualized desktop environments. Our analysis reveals that the root cause originates from the lack of verification and control over the third-party cryptographic libraries that they adopt. In order to demonstrate this, we implemented a cache side-channel attack on RSA in GnuPG and then conducted an evaluation of the vulnerability of 13 MUAs that support email encryption in Ubuntu 14.04, 16.04 and 18.04. Based on our experiment, we found that 10 of these MUA programs (representing approximately 77% of existing MUA programs) allow the installation of a vulnerable version of GnuPG, even when the latest version of GnuPG, which is secure against most cache side-channel attacks, is in use. In order to substantiate the importance of the vulnerability we discovered, we conducted a FLUSH+RELOAD attack on these MUA programs and demonstrated that the attack restored 92% of the bits of the 2048-bit RSA private key when the recipients read a single encrypted email.

MUA (Mail User Agent) programs support email encryption functionality for providing confidentiality of the email contents. They encrypt email contents using email encryption standards such as OpenPGP or S/MIME, mostly implemented by GnuPG, or GPG in practice. In order to understand security implication of the structures and analyze any possible vulnerabilities of MUA programs, in this paper, we investigated practical MUAs supporting e-mail encryption. As a result, we found severe vulnerabilities in a list of MUAs that allow cache side-channel attacks in virtualized desktop environments. Our analysis reveals that the root cause originates from the lack of verification and control over third-party cryptographic libraries they adopt. In order to substantiate the importance of the vulnerability we found, we delivered FLUSH+RELOAD attack on those MUA programs and demonstrated that the attack restores 92% of the RSA private keys when recipients read a single encrypted email.

The Dark Web sites are operated over anonymity-preserving protocols like Tor, making users of the Dark Web services more resilient to identification and monitoring. Although some previous works have focused on understanding the size of the Dark Web services and investigating their criminal activities, there is a lack of research on chronological analysis and in-depth profiling of the Dark Web sites, particularly in South Korea. Therefore, in this study, we implemented a Dark Web crawling system, and collected seed and sub Dark Web URLs using it. Then, the 3,000 Dark Web sites from the seed URLs were selected and their web pages were captured for profiling. An in-depth analysis was then conducted on the collected 3,000 Dark Web sites, and an intensive categorization was performed on the basis of their major criminal activities. We then carried out an in-depth profiling for top 3 Korean Dark Web sites to investigate cyber criminal activities in South Korea. In the profiling, criminal activities were collected and analyzed in a chronological point of view. Personal information leakage and Sybil IDs in the Dark Web were also identified based on the PGP keys we collected.

With the proliferation of new mobile devices, mobile cloud computing technology has emerged to provide rich computing and storage functions for mobile users. The explosive growth of mobile data has led to an increased demand for solutions that conserve storage resources. Data deduplication is a promising technique that eliminates data redundancy for storage. For mobile cloud storage services, enabling the deduplication of encrypted data is of vital importance to reduce costs and preserve data confidentiality. However, recently proposed solutions for encrypted deduplication lack the desired level of security and efficiency. In this paper, we propose a novel scheme for serverless efficient encrypted deduplication (SEED) in mobile cloud computing environments. Without the aid of additional servers, SEED ensures confidentiality, data integrity, and collusion resistance for outsourced data. The absence of dedicated servers increases the effectiveness of SEED for mobile cloud storage services, in which user mobility is essential. In addition, noninteractive file encryption with the support of lazy encryption greatly reduces latency in the file-upload process. The proposed indexing structure (D-tree) supports the deduplication algorithm and thus makes SEED much more efficient and scalable. Security and performance analyses prove the efficiency and effectiveness of SEED for mobile cloud storage services.

A secure deduplication technique removes duplicate data and stores only single copy to efficiently utilize the storage while guaranteeing the privacy of the data. Thus, it is a necessary technology for resource-limited for devices to save storages. However, most of the existing deduplication schemes based on convergent encryption suffer from 1) a convergent encryption key management problem and 2) a dynamic ownership management problem. In key management, convergent encryption generates a number of encryption keys whose size increases linearly with the number of distinct data. In terms of dynamic ownership management, although the ownership of data in a fog device or cloud storage frequently changes in real-world applications, supporting ownership changes are difficult because the convergent encryption keys are only bound to the data. In order to solve these problems, we present a secure deduplication scheme that features reliable and scalable key management based on pairing-based cryptography and supports dynamic ownership management. The proposed scheme avoids additional costs associated with distributing key components on secure channels and ownership keys on the user side yet guarantees secure key and ownership management.

In cloud-assisted data outsourcing systems, the privacy of sensitive data is a major concern. Thus, data are uploaded in encrypted form in many cloud applications while providing some basic yet critical functionalities, such as the ability to search. Similarity search over encrypted data provides decryptionless similarity testing between data and search queries which are encrypted by the data owner and users, respectively. However, previous similarity search schemes supporting multi-user settings incur unreasonable communication costs between the users and data owners during the search. In this paper, we propose efficient multi-user similarity search schemes for cloud storage. Specifically, the proposed schemes enable flexible similarity searches over encrypted data even when the given data have different format, encoding, or editing. The proposed similarity search schemes can guarantee asymptotically optimal performance for multi-user settings. We rigorously prove the proposed schemes are adaptively semantic secure. We also conduct an experimental analysis to demonstrate the applicability of the proposed scheme in practical cloud systems.

The explosion in the volume of data generated by end-point devices, arising from IoT proliferation, has lead to the adoption of data outsourcing to dedicated data centers. However, centralized data centers such as cloud storage cannot afford to manage large stores of data in a timely manner. To allow low latency access to large amounts of data, a new computing paradigm, called fog computing, has been introduced. In a fog computing environment, privacy issues surrounding outsourced data become more critical due to its complicated innards of the system. In addition, efficient resource management is another important criterion considering the application of pay-per-use in commercial fog storage. As an extension of cloud storage, most fog storage service providers will choose to adopt data deduplication techniques to minimize resource dissipation. At the same time, data owners may update or remove outsourced data stored in the remote storage to reduce expenses. In this paper, we propose the first privacy-preserving deduplication protocol capable of efficient ownership management in fog computing. It achieves fine-grained access control by introducing user-level key management and update mechanisms. Data-invariant user-level private keys enable data owners to maintain a constant number of keys regardless of the number of outsourced data files. The update of user-level public keys for valid data owners at the remote storage dramatically reduces communication overhead. Security and performance analyses demonstrate the efficiency of the proposed scheme in terms of communication and key management in fog storage.

Data deduplication has attracted many cloud service providers (CSPs) as a way to reduce storage costs. Even though the general deduplication approach has been increasingly accepted, it comes with many security and privacy problems due to the outsourced data delivery models of cloud storage. To deal with specific security and privacy issues, secure deduplication techniques have been proposed for cloud data, leading to a diverse range of solutions and trade-offs. Hence, in this article, we discuss ongoing research on secure deduplication for cloud data in consideration of the attack scenarios exploited most widely in cloud storage. On the basis of classification of deduplication system, we explore security risks and attack scenarios from both inside and outside adversaries. We then describe state-of-the-art secure deduplication techniques for each approach that deal with different security issues under specific or combined threat models, which include both cryptographic and protocol solutions. We discuss and compare each scheme in terms of security and efficiency specific to different security goals. Finally, we identify and discuss unresolved issues and further research challenges for secure deduplication in cloud storage.

As autonomous driving emerges as one of the challenges towards fifth (5G) generation mobile networks, vehicle-to-everything (V2X) communication attracts more attention recently. V2X includes several scenarios such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-pedestrian (V2P) as well as vehicle-to-network (V2N). V2X is expected to bring new services: automated driving, road safety service, traffic efficiency service, and digitalization of logistics. V2X has been standardized in the IEEE 802.11p and 3GPP, and further discussed in 5G automotive association (AA).

Searchable encryption (SE) supports privacy-preserving searches over encrypted data. Recent studies on SE have focused on improving efficiency of the schemes. However, it was shown that most of the previous SE schemes could reveal the client's queries even if they are encrypted, thereby leading to privacy violation. In order to solve the problem, several forward private SE schemes have been proposed in a single client environment. However, the previous forward private SE schemes have never been analyzed in multi-client settings. In this paper, we briefly review the previous forward private SE schemes. Then, we conduct a comparative analysis of them in terms of performance and forward privacy. Our analysis demonstrates the previous forward secure SE schemes highly depend on the file-counter. Lastly, we show that they are not scalable in multi-client settings due to the performance and security issue from the file-counter.

With the successful proliferation of data outsourcing services, security and privacy issues have drawn significant attention. Data authentication in particular plays an essential role in the storage of outsourced digital content and keeping it safe from modifications by inside or outside adversaries. In this paper, we focus on online data authentication using a Merkle (hash) tree to guarantee data integrity. By conducting in-depth diagnostics of the side channels of the Merkle tree-based approach, we explore novel solutions to improve the security and reliability of the maintenance of outsourced data. Based on a thorough review of previous solutions, we present a new method of inserting auxiliary random sources into the integrity verification proof on the prover side. This prevents the exposure of partial information within the tree structure and consequently releases restrictions on the number of verification execution, while maintaining desirable security and reliability of authentication for the long run. Based on a rigorous proof, we show that the proposed scheme maintains consistent reliability without being affected by continuous information leakage caused by repetitions of the authentication process. In addition, experimental results comparing with the proposed scheme with other state-of-the-art studies demonstrate its efficiency and practicality.

Public key encryption with keyword search (PEKS) enables users to search over encrypted data outsourced to an untrusted server. Unfortunately, updates to the outsourced data may incur information leakage by exploiting the previously submitted queries. Prior works addressed this issue by means of forward privacy, but most of them suffer from significant performance degradation. In this paper, we present a novel forward private PEKS scheme leveraging Software Guard Extension (SGX), a trusted execution environment provided by Intel. The proposed scheme presents substantial performance improvements over prior work. Specifically, we reduce the query processing cost from O(n) to O(1), where n is the number of encrypted data. According to our performance analysis, the overall computation time is reduced by 80% on average. Lastly, we provide a formal security definition of SGX-based forward private PEKS, as well as a rigorous security proof of the proposed scheme.

A major challenge achieving scalable access control for a large number of subscribers in a public broadcast is to distribute key update messages reliably to all stateless receivers. However, in a public broadcast, the rekeying messages can be dropped or compromised during transmission over an insecure broadcast channel, or transmitted to receivers while they were off-line. In this study, we propose a novel group key management scheme. It features a mechanism to allow legitimate receivers to recover the current group key, even if they lose key update messages for long-term sessions. The scheme uses short hint messages and member computation. Performance analysis shows that the proposed scheme has the advantages of scalability and efficient rekeying compared to previous reliable group key distribution schemes. The proposed key management scheme targets a conditional access system in a media broadcast in which there is no feedback channel from receivers to the broadcasting station.

With the dramatic increase of the number of mobile devices such as smartphones and tablet PCs, mobile traffic has increased enormously. Especially, the multimedia data accounts for bulk of the traffic transmitted in mobile networks. To accommodate this growth, device-to-device connection (D2D), which provides infra-connection off-loading, is receiving significant attention. However, we have observed that the majority of the current D2D protocols including Bluetooth and Wi-Fi Direct are vulnerable to man-in-the-middle (MITM) and replay attacks in mobile multi-hop networks. To resolve this problem, in this paper, we propose a novel D2D authentication protocol with a secure initial key establishment using ciphertext-policy attribute-based encryption (CP-ABE). By leveraging CP-ABE, the proposed scheme allows the communicating parties to mutually authenticate and derive the link key in an expressive and secure manner in a multi-hop network environment. We also propose several variations of the proposed scheme for different scenarios in a multi-hop networks without network infrastructure. We prove that the proposed scheme is secure against MITM and replay attack in D2D mobile multi-hop networks. Experimental results indicate that the proposed scheme incurs reasonable computation cost in the real world.

Data prefetching is a hardware-based optimization mechanism used in most of the modern microprocessors. It fetches data to the cache before it is needed. In this paper, we present a novel microarchitectural attack that exploits the prefetching mechanism. Our attack targets Instruction pointer (IP)-based stride prefetching in Intel processors. Stride prefetcher detects memory access patterns with a regular stride, which are likely to be found in lookup table-based cryptographic implementations. By monitoring the prefetching activities near the lookup table, attackers can extract sensitive information such as secret keys from victim applications. This kind of leakage from prefetching has never been considered in the design of constant time algorithm to prevent side-channel attacks. We show the potential of the proposed attack by applying it against the Elliptic Curve Diffie-Hellman (ECDH) algorithm built upon the latest version of OpenSSL library. To the best of our knowledge, this is the first microarchitectural side-channel attack exploiting the hardware prefetching of modern microprocessors.

Honey encryption (HE) is a novel password-based encryption scheme that is secure against brute-force attacks even if users’ passwords have min-entropy. However, in HE, decryption with an incorrect key produces fake messages that appear valid. Hence, password typographical errors may confuse even legitimate users. This has been one of the most challenging problems in HE. To tackle this challenge, we propose three types of protocols that enable legitimate users to detect password typographical errors in HE. We conducted a theoretical analysis and performed an IRB-approved user study with 150 participants to compare the performance of each scheme. We also analyzed the security of the proposed schemes against online and offline brute-force attacks. The results from the user study and theoretical analysis show that the proposed schemes can effectively solve the typographical error problem of HE, which can detect typographical errors with 99% accuracy.

For building a secure cloud storage service on top of a public cloud infrastructure, attribute-based encryption (ABE) has been a preferred solution due to its flexible access control. ABE, however, incurs heavy computation cost on users during decryption. Thus, previous studies solved this problem by enabling cloud servers to perform a part of decryption operations on behalf of the users. In order to empower users to verify the correctness of the delegated decryption by the cloud, they employed a cryptographic commitment or message authentication code (MAC) to enable users to check the correctness of partial decryption of the cloud. However, the previous schemes fail to ensure the correctness of computation in the presence of malicious cloud servers. In this paper, we propose a novel and generic commitment scheme for ABE, which is secure against tampering attacks by malicious cloud servers. According to the performance analysis, the proposed scheme is only 0.5 ms slower on average than the previous commitment-based schemes and two to three times faster than the MAC-based scheme.

To achieve high storage saving, data deduplication techniques are widely used in many practical cloud storage services, which removes redundant data and keeps only a single copy of them. However, secure data deduplication over encrypted data is challenging since encryption may result in different ciphertexts even when the original messages are the same. Thus, message-locked encryption (MLE) is proposed to solve this issue and demonstrates that it is secure under the unpredictable message set. Since block-level deduplication can achieve more fine-grained storage saving, several block-level deduplication schemes that support updatability are also vividly proposed. However, the previous updatable block-level MLE schemes are vulnerable against brute-force attack when the message set is predictable. Since the size of a block is typically much less than an arbitrary size of a file, the predictability problem is a very important pragmatic concern which should be addressed in the block-level deduplication literature. In this paper, thus, we propose a novel secure block-level deduplication scheme that guarantees efficient data update and brute-force attack resilience even when messages are predictable with the rigorous security proof. Also, our performance evaluation shows that additional time and bandwidth usage can be minimized as the size of a block increases.

Bitcoin mixing services improve anonymity by breaking the connection between Bitcoin addresses. In the darkweb environment, many illegal trades, such as in drugs or child pornography, avoid their transactions being traced by exploiting mixing services. Therefore, de-mixing algorithms are needed to identify illegal financial flows and to reduce criminal activity. Unfortunately, to the best of our knowledge, few studies on analyzing mixing services and de-anonymizing transactions have been proposed. In this paper, we conduct an in-depth analysis of real-world mixing services, and propose a de-mixing algorithm for Helix, one of the most widely used Bitcoin mixing services. The proposed algorithm de-anonymizes the relationship between the input and output addresses of mixing services by exploiting the static and dynamic parameters of mixing services. Our experiment showed that, we could identify the relationships between the input and output addresses of the Helix mixing service with a 99.14% accuracy rate.

Arazi was the first author to propose the integration of a key exchange protocol with a digital signature algorithm. Other authors have subsequently proposed methods to increase the level of security and achieve the necessary properties of authenticated key exchange protocols. However, these proposals exhibit several weaknesses and the majority of these protocols are based only on a single hard problem. In this paper, we propose a new secured key exchange protocol which is based on two hard problems. We then extend this to a signcryption protocol and a deniable signcryption protocol. These protocols represent the first steps in a new direction, but they are designed based on two hard problems. The security proofs for the new protocols confirm their novelty and security.

Explosion of multimedia content brings forth the needs of efficient resource utilization using the state of the arts cloud computing technologies such as data deduplication. In the cloud computing environments, achieving both data privacy and integrity is the challenging issue for data outsourcing service. Proof of Storage with Deduplication (POSD) is a promising solution that addresses the issue for the cloud storage systems with deduplication enabled. However, the validity of the current POSD scheme stands on the strong assumption that all clients are honest in terms of generating their keys. We present insecurity of this approach under new attack model that malicious clients exploit dishonestly manipulated keys. We also propose an improved POSD scheme to mitigate our attack.

Recently, there has been an explosive increase in the volume of multimedia data that is available as a result of the development of multimedia technologies. More and more data is becoming available on a variety of web sites, and it has become increasingly cost prohibitive to have a single data server store and process multimedia files locally. Therefore, many service providers have been likely to outsource data to cloud storage to reduce costs. Such behavior raises one serious concern: how can data users be authenticated in a secure and efficient way? The most widely used password-based authentication methods suffer from numerous disadvantages in terms of security. Multi-factor authentication protocols based on a variety of communication channels, such as SMS, biometric, or hardware tokens, may improve security but inevitably reduce usability. To this end, we present a data block-based authentication scheme that is secure and guarantees usability in such a manner where users do nothing more than enter a password. In addition, the proposed scheme can be effectively used to revoke user rights. To the best of our knowledge, our scheme is the first data block-based authentication scheme for outsourced data that is proven to be secure without degradation in usability. An experiment was conducted using the Amazon EC2 cloud service, and the results show that the proposed scheme guarantees a nearly constant time for user authentication.

In recent years, the volume of educational contents has been explosively increased thanks to the rapid development of multimedia technologies. Furthermore, the development of smart devices has made various educational institutes use them as effective learning tools. Since more and more educational contents become available not only at school zone but at a variety of online learning systems, it becomes increasingly unaffordable for a single educational contents provider to store and process them locally. Therefore, many educational contents providers are likely to outsource the contents to cloud storage for cost saving. These phenomena raise one serious concern: how to authenticate educational contents users in a secure and efficient way? The most widely used password-based authentication suffers from numerous drawbacks in terms of security. Multi-factor authentication protocols based on diverse communication channels such as SMS, biometric, hardware token could enhance security, however they inevitably bring poor usability. To this end, we present a data block-based authentication scheme, which provides provable security and guarantees usability invariant such that users do nothing but entering a password. In addition, the proposed scheme supports efficient user revocation. To the best of our knowledge, our scheme is the first data block-based authentication scheme for outsourced educational contents that is provably secure without usability degradation. The experiment on Amazon EC2 cloud shows that the proposed scheme guarantees nearly constant time for user authentication.

Increment of multimedia data motivates users to utilize cloud storage (CS) to exploit its massive size. For this extensible storage system, there are two desirable requirements: (1) the users should be able to ensure that their outsourced data is securely protected and (2) the cloud service provider should be able to eliminate redundant copies of data for improvement of storage utilization. Conventional encryption scheme does not satisfy the deduplication on ciphertext as it destroys message equality. Recent study, DupLESS, has enhanced Convergent Encryption (CE) and provided strong privacy. However, CE-based scheme allows the users to possibly decrypt cloud data even if the user loses his ownership to the data. In order to solve this problem, we propose a secure deduplication scheme with user revocation. Our scheme leverages oblivious pseudo-random function to generate encryption key. The CS enforces data access policy using privilege-based encryption to provide user revocation. The security analysis proves that the proposed scheme is secure against unauthorized decryption by revoked users or the cloud server, and brute-force attack on predictable set of data.

A wireless electrocardiogram case study suggests that current CDMA2000 cellular technology has considerable potential in medical telemetry. Modifications to the network protocol stack ensure the highest data integrity and lowest service delay.

This paper proposes a novel hybrid model that integrates the synergy of two superior classifiers for functional magnetic resonance imaging (fMRI) recognition, namely, convolutional neural networks (CNNs) and support vector machines (SVMs), both of which have proven results in the field of image recognition. In the proposed model, the CNN functions as a trainable feature extractor and the SVM functions as a recognizer. This hybrid model extracts features from raw images and generates predictions for fMRI recognition. We conducted experiments on Haxby's 2001 fMRI dataset. Comparisons with Haxby's study using the same database indicated that the proposed fusion achieved superior recognition accuracy of 99.5% compared to the Haxby's approach. Further, when the CNN was used as a feature extractor, the SVM classifier was demonstrated to be the best combining counterpart, providing the best synergy effect in terms of accuracy. This is compared with other classifiers based on learning algorithms such as decision tree, neural network, K-nearest neighbor, random forest, and AdaBoost.

TrapFetch is trained by monitoring the read requests issued by an application. It detects bursts of disk reads, determines the appropriate addresses at which breakpoints should be inserted in the application and library codes prior to the bursts of reads, and then logs this information with the data requested during the interval between each consecutive pair of breakpoints. When the application and library codes are loaded from the disk into memory, TrapFetch inserts breakpoints at the designated addresses based on the logs. During subsequent runs, TrapFetch is invoked at each breakpoint when it prefetches the corresponding data into the page cache. This approach is effective during both launch and run-time. TrapFetch operates at the user level, thus avoiding interference with other applications. In experiments on five popular applications (FlightGear, SpeedDreams 2, Pillars of Eternity, Eclipse, and VegaStrike), TrapFetch reduced the time for launch by up to 39.7% and time for run-time data-loading by up to 63.7%.

With the increasing number of users enrolled, biometric identification requires more computing resources to scan all records of a database and locate the best match. As such, database owners are willing to delegate user biometric information (in encrypted state) to the cloud to enroll and identify users, while preserving privacy. Wang et al. proposed a cloud-based privacy-preserving biometric scheme, a.k.a. CloudBI, in ESORICS 2015, but their security assumption does not capture practical aspects of real world attacks. In this paper, we show how an attack enrolls fake biometric data and then manipulates them to recover encrypted an identification request in CloudBI. Next, we propose an effective security patch to CloudBI, which is secure against enrollment-level attackers. Experimental results show that the proposed security patch bring about little performance degradation to CloudBI.

In this paper, we examine the online authentication method based on Merkle (hash) tree focusing on its reliability. Coming from side channels in online authentication, the effectiveness runs into danger in the long run. With consideration of effectiveness, we present a Merkle tree based online authentication resilient against side channels by obfuscating authentication proofs. Security and efficiency are analyzed to demonstrate the practicality of the proposed approach.

Secure deduplication using convergent encryption eliminates duplicate data and stores only one copy to save storage costs while preserving the security of the outsourced data. However, convergent encryption produces a number of encryption keys, of which size is linear to the number of different data. Although a deduplication scheme has been proposed for efficient convergent key management recently, it has drawbacks in terms of scalability and key management security. In order to solve these problems, we propose a novel secure deduplication scheme with scalable and reliable key management based on paring-based cryptography. The proposed scheme does not require additional secure channels to distribute key components while still guaranteeing secure key management as opposed to the previous schemes.

In cloud storage services, deduplication technology is commonly used to reduce the space and bandwidth requirements of services by eliminating redundant data and storing only a single copy of them. Deduplication is most effective when multiple users outsource the same data to the cloud storage, but it raises issues relating to security and ownership. Proof-of-ownership schemes allow any owner of the same data to prove to the cloud storage server that he owns the data in a robust way. However, many users are likely to encrypt their data before outsourcing them to the cloud storage to preserve privacy, but this hampers deduplication because of the randomization property of encryption. Recently, several deduplication schemes have been proposed to solve this problem by allowing each owner to share the same encryption key for the same data. However, most of the schemes suffer from security flaws, since they do not consider the dynamic changes in the ownership of outsourced data that occur frequently in a practical cloud storage service. In this paper, we propose a novel server-side deduplication scheme for encrypted data. It allows the cloud server to control access to outsourced data even when the ownership changes dynamically by exploiting randomized convergent encryption and secure ownership group key distribution. This prevents data leakage not only to revoked users even though they previously owned that data, but also to an honest-but-curious cloud storage server. In addition, the proposed scheme guarantees data integrity against any tag inconsistency attack. Thus, security is enhanced in the proposed scheme. The efficiency analysis results demonstrate that the proposed scheme is almost as efficient as the previous schemes, while the additional computational overhead is negligible.

This paper proposes a conceptually simple but practical management of outsourced data for provably secure duplicate elimination with efficient storage utilization. We focus on cutting off dependency of encryption key on underlying plain data, making data owners relieved from information leakage as long as their keys are kept secret. Rather than forcing data owners for the same content to share the same key, we restrict storage space in which duplicate identification and elimination occur in order to increase (de)duplication ratio. This allows data owners to choose their own keys with no restriction, and eventually the security of the proposed approach would not be affected by other data owners as well as inside and/or outside adversaries. Our approach can be adaptively applied to existing remote storage systems according to storage capacity and security requirements.