Untrusted Server Research Articles

Trust-Based Secure Multi-Cloud Collaboration Framework in Cloud-Fog-Assisted IoT

Cloud-Fog-Assisted Internet-of-Things (IoT) is a convincing paradigm to provide users with on-demand and low-latency services through Fog nodes in the edge of multiple clouds (Multi-Cloud). Multi-Cloud is a scalable multi-domain service-oriented net-centric system and can respond to complicated user requirements leveraging Multi-Cloud Service Composition (MCSC). However, in MCSC, user security can be easily compromised by untrusted and curious cloud service providers that may collect and violate the privacy and other essential assets of cloud users. Although many trust-based MCSC solutions have been proposed to seek a trustworthy composite service with highest trust level, most of them are vulnerable to malicious users intending to break through the clouds and inflict serious data leakage or asset damage. Considering these security concerns on both malicious users and untrusted service providers, in this paper, we present a trust-based secure multi-cloud collaboration framework for Cloud-Fog-Assisted IoT systems. Specifically, to guarantee the security of users, we develop a role-based trust evaluation method to enhance the trustworthiness of MCSC. To preserve the security of services, we design an efficient user authentication scheme and a secure collaboration scheme to provide collaborative user authentication and access control mechanism for MCSC. We evaluate its performane with extensive experiments

GlassDB: An Efficient Verifiable Ledger Database System Through Transparency

Verifiable ledger databases protect data history against malicious tampering. Existing systems, such as blockchains and certificate transparency, are based on transparency logs --- a simple abstraction allowing users to verify that a log maintained by an untrusted server is append-only. They expose a simple key-value interface without transactions. Building a practical database from transparency logs, on the other hand, remains a challenge. In this paper, we explore the design space of verifiable ledger databases along three dimensions: abstraction, threat model, and performance. We survey existing systems and identify their two limitations, namely, the lack of transaction support and the inferior efficiency. We then present GlassDB, a distributed database system that addresses these limitations under a practical threat model. GlassDB inherits the verifiability of transparency logs, but supports transactions and offers high performance. It extends a ledgerlike key-value store with a data structure for efficient proofs, and adds a concurrency control mechanism for transactions. GlassDB batches independent operations from concurrent transactions when updating the core data structures. In addition, we design a new benchmark for evaluating verifiable ledger databases, by extending YCSB and TPC-C benchmarks. Using this benchmark, we compare GlassDB against four baselines: reimplemented versions of three verifiable databases, and a verifiable map backed by a transparency log. Experimental results demonstrate that GlassDB is an efficient, transactional, and verifiable ledger database system.

PriFace: a privacy-preserving face recognition framework under untrusted server

PriFace: a privacy-preserving face recognition framework under untrusted server

On the Security of Offloading Post-Processing for Quantum Key Distribution.

Quantum key distribution (QKD) has been researched for almost four decades and is currently making its way to commercial applications. However, deployment of the technology at scale is challenging because of the very particular nature of QKD and its physical limitations. Among other issues, QKD is computationally intensive in the post-processing phase, and devices are therefore complex and power hungry, which leads to problems in certain application scenarios. In this work, we study the possibility to offload computationally intensive parts in the QKD post-processing stack in a secure way to untrusted hardware. We show how error correction can be securely offloaded for discrete-variable QKD to a single untrusted server and that the same method cannot be used for long-distance continuous-variable QKD. Furthermore, we analyze possibilities for multi-server protocols to be used for error correction and privacy amplification. Even in cases where it is not possible to offload to an external server, being able to delegate computation to untrusted hardware components on the device itself could improve the cost and certification effort for device manufacturers.

Preventing a Cryptoapocalypse: From mathematics to circuits for postquantum cryptography

Communication over the Internet is an integral part of the connected world. As the Internet is a public network, the data packets exchanged by the communicating parties pass through various insecure channels and untrusted servers before they reach their destinations. Still, we feel it is safe to send e-mails, visit social media websites, watch movies of our own preferences online, and shop online using credit cards. Cryptography or encryption makes it possible to protect our private information in the presence of third parties. When we browse a secured website, we see a lock symbol on the browser; it means our communication with the website is encrypted, and hence, no third party will be able to read the data packets exchanged between the browser and the website. Hypertext Transfer Protocol Secure (HTTPS) is used to establish an encrypted, hence secured communication channel between a web browser and a website. The browser website is just one example; cryptography is happening in your mobile phones, smart cards, IoT devices, and in almost all connected devices.

Backward private dynamic searchable encryption with update pattern

Symmetric Searchable Encryption (SSE) realizes to outsource data to an untrusted server privately. Dynamic SSE (DSSE) is a practical application which supports data addition and deletion. However, these update operations incur leakage during search and update queries. Therefore, forward and backward privacy were introduced to address the update and search operation leakage in DSSE. Forward privacy was extensively studied in the literature. Backward privacy with three types of revealing level was studied little. The strongest backward privacy was achieved by the expensive tools, impractical! Wheras the practical DSSEs gained weak backward privacy only. In this paper, we present a practical DSSE scheme to achieve stronger backward security—backward privacy with update pattern. We take advantage of key-homomorphic pseudorandom functions and our scheme requires only 1 roundtrip to get search result.

Collecting Multidimensional Numerical Data and Estimating Mean with Personalized Local Differential Privacy

Collecting and analyzing data can generate a wealth of knowledge, but it can also raise privacy concerns. Local differential privacy (LDP) is the latest privacy standard to address this issue and has been implemented on platforms such as Chrome, iOS, and macOS. In the LDP solution, users first perturb their own data on the user side and then upload the perturbed data to the server. This not only protects against background attacks but also against untrusted servers. However, existing multidimensional solutions ignore the personalized privacy needs of users. In this paper, we meet the personalized privacy needs of users while reducing the mean square error of the perturbed data. Specifically, we first designed a personalized privacy budget allocation within a certain range, which meets the personalized privacy needs of users. Then, we optimized the sampling dimension of the existing solution, which resulted in a smaller mean square error of the perturbed data. Finally, we proposed our solution for collecting multidimensional numerical data and estimating the mean. In addition, we conducted experiments on two real datasets. The results demonstrate that the mean square error of our solution is lower than the existing solutions.

Novel trajectory privacy protection method against prediction attacks

When the public uses Location-based Services (LBSs), their location information is constantly exposed. Owing to the spatiotemporal correlation of trajectories, it is easy for attackers to use historical trajectories and background knowledge to predict future locations of target users. We refer to this type of inference attack as a trajectory prediction attack. To address such potential but threatening attacks in a continuous location query, we propose a novel trajectory privacy protection method. The proposed algorithm aims to generate an indistinguishable perturbed location that is robust to the prediction attack, wherein the user’s real location can be replaced by a perturbed location when submitted to an untrusted server. First, a hidden Markov model-based trajectory prediction mechanism is proposed to simulate predictive attacks and compute the predictability of positions before the trajectory is released. Second, the w sliding window mechanism is designed to dynamically adjust the privacy protection degree of each location point in the trajectory according to the predictability of the location and privacy needs of users. Finally, we propose a bounded noise-adding algorithm based on the Laplace mechanism to improve the usability of data. In our experiments, mutual information, trajectory root-mean-square error, query error, and root-mean-square predictability were used as evaluation criteria, and the performance of the proposed method was comprehensively evaluated. The results show that our algorithm can reduce the trajectory predictability to 0.21 without reducing data availability, which is effective against prediction attacks.

Location Privacy-Preserving Query Scheme Based on the Moore Curve and Multi-User Cache

With the rapid development of the Internet of Things, location-based services have emerged in many social and business fields. In obtaining the service, the user needs to transmit the query data to an untrusted location service provider for query and then obtain the required content. Most existing schemes tend to protect the user’s location privacy information while ignoring the user’s query privacy. This paper proposes a secure and effective query privacy protection scheme. The multi-user cache is used to store historical query results, reduce the number of communications between users and untrusted servers, and introduce trust computing for malicious users in neighbor caches, thereby reducing the possibility of privacy leakage. When the cache cannot meet the demand, the user’s location coordinates are converted using the Moore curve, processed using encryption technology, and sent to the location service provider to prevent malicious entities from accessing the transformed data. Finally, we simulate and evaluate the scheme on real datasets, and the experimental results demonstrate the safety and effectiveness of the scheme.

Accelerating Decentralized and Partial-Privacy Data Access for VANET via Online/Offline Functional Encryption

Vehicles share information via Vehicular Ad-hoc Network (VANET) that is considered the foundation of the intelligent transportation system. Access control from functional encryption (FE) allows secure sharing over encrypted data with both utilization and privacy. However, low-powerful vehicles and the untrusted server incurs that FE-based access control mechanisms are less practical with client-side inefficiency and access unreliability. In this work, we put forth ORAC, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">an <u>O</u>ptimized encrypted <u>A</u>ccess <u>C</u>ontrol system with partial privacy and <u>R</u>eliability</i> . ORAC is built on top of functional encryption that guarantees data partial privacy and data utilization in VANET, and separates the data access into the offline phase and the online phase, such that most of decryption computation could be done in the offline phase and vehicles could execute the real decryption efficiently. By embedding blockchain transactions into identity records and data storage, ORAC protects data accessed by vehicles from tampering and thereby manages data transfers with high reliability. It can be instantiated directly from pairing-based assumptions and features only the decryption complexity of two exponentiation and two multiplication operations. We give a formal security proof of the instantiation in the semantic security model. We also prototype our scheme by implementing ORAC over Ethereum with two types of smart contracts and the evaluation can demonstrate its efficiency optimization.

A Survey on Approach for Efficient and Secure Data Encryption Scheme for Spatial Data

Abstract: In spatial applications location data of the users continuously processed in un-trusted servers, this may cause to user privacy. Location information of the users is very sensitive data like their meeting information, residential and official location information’s. In present solutions of the un-trusted servers may have the several problems and limitation like data security, data availability, request processing etc. In general cloud environment provides lot of features to solve many problems with low cost. Elastic cloud environment will provide successful solutions for spatial data and user’s data. Many security algorithms were proposed and for cloud data security and many Locations Based services (LBS) security functionalities were proposed in many researches. In this paper we survey on various secure location-based techniques and cloud data security architectures.

An Architecture to Accelerate Computation on Encrypted Data

Fully homomorphic encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted servers. Though it provides ideal security, FHE is prohibitively expensive when executed in software. These overheads are a major barrier to FHE's widespread adoption. We present F1, the first FHE accelerator that is capable of executing full FHE programs. F1 builds on an in-depth architectural analysis of the characteristics of FHE computations that reveals acceleration opportunities. F1 is a wide-vector processor with novel functional units deeply specialized to FHE primitives, such as modular arithmetic, number-theoretic transforms, and structured permutations. This organization provides so much compute throughput that data movement becomes the bottleneck. Thus, F1 is primarily designed to minimize data movement. F1 is the first system to accelerate complete FHE programs, and outperforms state-of-the-art software implementations by gmean 5,400x. These speedups counter FHE's overheads and enable new applications, like real-time private deep learning in the cloud.

Parallel and private generalized suffix tree construction and query on genomic data

BackgroundSeveral technological advancements and digitization of healthcare data have provided the scientific community with a large quantity of genomic data. Such datasets facilitated a deeper understanding of several diseases and our health in general. Strikingly, these genome datasets require a large storage volume and present technical challenges in retrieving meaningful information. Furthermore, the privacy aspects of genomic data limit access and often hinder timely scientific discovery.MethodsIn this paper, we utilize the Generalized Suffix Tree (GST); their construction and applications have been fairly studied in related areas. The main contribution of this article is the proposal of a privacy-preserving string query execution framework using GSTs and an additional tree-based hashing mechanism. Initially, we start by introducing an efficient GST construction in parallel that is scalable for a large genomic dataset. The secure indexing scheme allows the genomic data in a GST to be outsourced to an untrusted cloud server under encryption. Additionally, the proposed methods can perform several string search operations (i.e., exact, set-maximal matches) securely and efficiently using the outlined framework.ResultsThe experimental results on different datasets and parameters in a real cloud environment exhibit the scalability of these methods as they also outperform the state-of-the-art method based on Burrows-Wheeler Transformation (BWT). The proposed method only takes around 36.7s to execute a set-maximal match whereas the BWT-based method takes around 160.85s, providing a 4× speedup.

A potential game-based outsourcing computation framework for multiple calculators

Outsourcing computation enables a computationally weak client to outsource the computation of a function f to a more powerful but untrusted server. The traditional outsourcing computation model forbids communication between players, but it has little effect. Based on the game theory, this paper establishes an outsourcing computation model which is more in line with the actual scene. Firstly, we construct a structural mapping relationship between security outsourcing computation and the optimization problem. Secondly, by designing the individual potential function and the global potential function, the individual goal is consistent with the global goal to ensure the correctness of the calculation results. Finally, in the information exchange environment between calculators, we construct a Zero-determinant strategy to ensure that the calculator chooses the strategy according to the predetermined target.

Secure contact tracing platform from simplest private set intersection cardinality

Contact tracing is an essential tool for controlling the spread of disease through human populations. However, existing contact tracing applications are either vulnerable to privacy and security attacks or heavy bandwidth/computational requirements on the client's devices. In this work, we introduce SecureCT, a Secure Contact Tracing platform with strong privacy protection and lightweight cost. SecureCT prevents linkage attacks, eliminates replay and relay attacks, and allows the phone's holder to delegate their contact tracing computation to untrusted servers while maintaining the user's privacy. The technical core of our scheme is an efficient Private Set Intersection Cardinality protocol which only relies on symmetric-key primitives. We evaluate its performance to show the feasibility of our proposed system in practice.

A Geographical and Social Society Attributes Based Privacy Preserving Recommendation Method for POIs

Point-of-interests (POIs) recommendation technology using user’s check-in data has attracted great attentions in recent years. However, user’s check-in data often contains sensitive information such as time and location data. Due to privacy considerations, many users are unwilling to share their check-in data with untrusted service providers, which has a great negative impact on recommendation quality. Trying to solve this problem, geographical and social society attributes based privacy preserving recommendation method for POIs, named GSSA-PPRM, is proposed in the paper. In the proposed method, a local differentially private matrix factorization algorithm is firstly designed to learn user’s preference with social attribute in client/server style. Then, according to the learned preference and considering geographical distance of POIs, a self-adaptive kernel density estimation algorithm is devised to study user’s check-in behavior. And an algorithm that tallies POI visit count and computes POI popularity by securely collecting user’s check-in data through random response (RR) mechanism is presented. Finally, a rating rule is given to predict the ratings of users for POIs by integrating kernel density estimation and POI popularity. The experimental results on two real datasets validate that the proposed method achieves better POI recommendation quality in condition of keeping user’s privacy.

MFPSE: Multi-user Forward Private Searchable Encryption with dynamic authorization in cloud computing

Dynamic Searchable Symmetric Encryption (DSSE) allows users to outsource data with ciphertext format to untrusted servers and supports the operations of data adding and deleting, which is adopted in common usage by government and business. Recently, the primitive of Forward Privacy in DSSE has drawn great public interest owing to its beneficial feature, which is that it can guard against the newly uploaded files from linking to previous search tokens. However, most of existing Forward Private Searchable Encryption (FPSE) schemes focus on single-user environment, which means that only the users themselves can utilize the data, greatly limits the wide application in cloud computing. To our knowledge, it is difficult to migrate the FPSE schemes to multi-user network. First, to realize forward privacy, the data owner should share the entire key group to the legitimate users, which means the users have the privilege of tampering or deleting data rather than query only; secondly, some FPSE schemes use the special structures and cannot be directly developed to multi-user network. Inspired by this, we present a scheme of multi-user forward private searchable encryption with dynamic authorization. The proposed scheme is based on a new structure of Multi-user State Chain and involves dynamic keyword-oriented authorization management. We prove our scheme can meet the secure characteristics, then conduct the performance evaluation and experiments. The results demonstrate that compared with the existing solutions, our scheme is superior in efficiency and practicability.

Towards efficient verifiable multi-keyword search over encrypted data based on blockchain.

Searchable symmetric encryption (SSE) provides an effective way to search encrypted data stored on untrusted servers. When the server is not trusted, it is indispensable to verify the results returned by it. However, the existing SSE schemes either lack fairness in the verification of search results, or do not support the verification of multiple keywords. To address this, we designed a multi-keyword verifiable searchable symmetric encryption scheme based on blockchain, which provides an efficient multi-keyword search and fair verification of search results. We utilized bitmap to build a search index in order to improve search efficiency, and used blockchain to ensure fair verification of search results. The bitmap and hash function are combined to realize lightweight multi-keyword search result verification, compared with the existing verification schemes using public key cryptography primitives, our scheme reduces the verification time and improves the verification efficiency. In addition, our scheme supports the dynamic update of files and realizes the forward security in update. Finally, formal security analysis proves that our scheme is secure against Chosen-Keyword Attacks (CKA), experimental analysis demonstrations that our scheme is efficient and viable in practice.

Private Decision Tree Evaluation by a Single Untrusted Server for Machine Learnig as a Service

Private Decision Tree Evaluation by a Single Untrusted Server for Machine Learnig as a Service

ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data

In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.