Advanced Cloud Privacy Threat Modeling

Privacy-preservation for sensitive data has become a challenging issue in cloud computing. Threat modeling as a part of requirements engineering in secure software development provides a structured approach for identifying attacks and proposing countermeasures against the exploitation of vulnerabilities in a system . This paper describes an extension of Cloud Privacy Threat Modeling (CPTM) methodology for privacy threat modeling in relation to processing sensitive data in cloud computing environments. It describes the modeling methodology that involved applying Method Engineering to specify characteristics of a cloud privacy threat modeling methodology, different steps in the proposed methodology and corresponding products. We believe that the extended methodology facilitates the application of a privacy-preserving cloud software development approach from requirements engineering to design

Approximate Two-Party Privacy-Preserving String Matching with Linear Complexity

Consider two parties who want to compare their strings, e.g., genomes, but do not want to reveal them to each other. We present a system for privacy-preserving matching of strings, which differs from existing systems by providing a deterministic approximation instead of an exact distance. It is efficient (linear complexity), non-interactive and does not involve a third party which makes it particularly suitable for cloud computing. We extend our protocol, such that it mitigates iterated differential attacks proposed by Goodrich. Further an implementation of the system is evaluated and compared against current privacy-preserving string matching algorithms.Comment: 6 pages, 4 figure

Geo-tagging and privacy-preservation in mobile cloud computing

With the emerge of the cloud computing service and the explosive growth of the mobile devices and applications, mobile computing technologies and cloud computing technologies have been drawing significant attentions. Mobile cloud computing, with the synergy between the cloud and mobile technologies, has brought us new opportunities to develop novel and practical systems such as mobile multimedia systems and cloud systems that provide collaborative data-mining services for data from disparate owners (e.g., mobile users). However, it also creates new challenges, e.g., the algorithms deployed in the computationally weak mobile device require higher efficiency, and introduces new problems such as the privacy concern when the private data is shared in the cloud for collaborative data-mining. The main objectives of this dissertation are: 1. to develop practical systems based on the unique features of mobile devices (i.e., all-in-one computing platform and sensors) and the powerful computing capability of the cloud; 2. to propose solutions protecting the data privacy when the data from disparate owners are shared in the cloud for collaborative data-mining. We first propose a mobile geo-tagging system. It is a novel, accurate and efficient image and video based remote target localization and tracking system using the Android smartphone. To cope with the smartphones' computational limitation, we design light-weight image/video processing algorithms to achieve a good balance between estimation accuracy and computational complexity. Our system is first of its kind and we provide first hand real-world experimental results, which demonstrate that our system is feasible and practicable. To address the privacy concern when data from disparate owners are shared in the cloud for collaborative data-mining, we then propose a generic compressive sensing (CS) based secure multiparty computation (MPC) framework for privacy-preserving collaborative data-mining in which data mining is performed in the CS domain. We perform the CS transformation and reconstruction processes with MPC protocols. We modify the original orthogonal matching pursuit algorithm and develop new MPC protocols so that the CS reconstruction process can be implemented using MPC. Our analysis and experimental results show that our generic framework is capable of enabling privacy preserving collaborative data-mining. The proposed framework can be applied to many privacy preserving collaborative data-mining and signal processing applications in the cloud. We identify an application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We further propose a privacy preserving storage and secure watermark detection framework by adopting our generic framework to address such a requirement. In our secure watermark detection framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a compressive sensing domain to protect the privacy. We also give mathematical and statistical analysis to derive the expected watermark detection performance in the compressive sensing domain, based on the target image, watermark pattern and the size of the compressive sensing matrix (but without the actual CS matrix), which means that the watermark detection performance in the CS domain can be estimated during the watermark embedding process. The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the compressive sensing domain is feasible. By taking advantage of our mobile geo-tagging system and compressive sensing based privacy preserving data-mining framework, we develop a mobile privacy preserving collaborative filtering system. In our system, mobile users can share their personal data with each other in the cloud and get daily activity recommendations based on the data-mining results generated by the cloud, without leaking the privacy and secrecy of the data to other parties. Experimental results demonstrate that the proposed system is effective in enabling efficient mobile privacy preserving collaborative filtering services.Includes bibliographical references (pages 126-133)

Crypto-analyses on “secure and efficient privacy-preserving public auditing scheme for cloud storage”

Recently, Worku et al. pointed out that the work “privacy-preserving public auditing for data storage security in cloud computing” proposed by Wang et al. is insecure and their second work “privacy- preserving public auditing for secure cloud the storage” is inefficient. Thus, they offered a secure and efficient-privacy public auditing scheme for cloud storage. They claimed that their system is provably secure in the random oracle model and the operation is effective. However, after crypto-analysis, we found that the scheme cannot reach the security goal, it has the existential forgery attack. We, therefore, alter it to incorporate the desired privacy preserving requirement, which is very significant in a privacy-preserving public auditing protocol for cloud storage

Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model. The biggest concern with DLaaS is user privacy if the input samples are sensitive data. We provide here an efficient privacy-preserving system by employing high-end technologies such as Fully Homomorphic Encryption (FHE), Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned applications. This comes at high cost as it requires enormous computing power. In this paper, we show how to accelerate the performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution achieved a sufficient security level (> 80 bit) and reasonable classification accuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms of latency, we could classify an image in 5.16 seconds and 304.43 seconds for MNIST and CIFAR-10, respectively. Our system can also classify a batch of images (> 8,000) without extra overhead

Cloud-based Privacy-Preserving Collaborative Consumption for Sharing Economy

Cloud computing has been a dominant paradigm for a variety of information processing platforms, particularly for enabling various popular applications of sharing economy. However, there is a major concern regarding data privacy on these cloud-based platforms. This work presents novel cloud-based privacy-preserving solutions to support collaborative consumption applications for sharing economy. In typical collaborative consumption, information processing platforms need to enable fair cost-sharing among multiple users for utilizing certain shared facilities and communal services. Our cloud-based privacy-preserving protocols, based on homomorphic Paillier cryptosystems, can ensure that the cloud-based operator can only obtain an aggregate schedule of all users in facility sharing, or a service schedule conforming to service provision rule in communal service sharing, but is unable to track the personal schedules or demands of individual users. More importantly, the participating users are still able to settle cost-sharing among themselves in a fair manner for the incurred costs, without knowing each other's private schedules or demands. Our privacy-preserving protocols involve no other third party who may compromise privacy. We also provide an extensive evaluation study and a proof-of-concept system prototype of our protocols.Comment: To appear in IEEE Trans. Cloud Computin

Privacy-preserving key-value store

Cloud computing is arguably the foremost delivery platform for data storage and data processing. It turned computing into a utility based service that provides consumers and enterprises with on-demand access to computing resources. Although advantageous, there is an inherent lack of control over the hardware in the cloud computing model, this may constitute an increased privacy and security risk. Multiple encrypted database systems have emerged in recent years, they provide the functionality of regular databases but without compromising data confidentiality. These systems leverage novel encryption schemes such as homomorphic and searchable encryp tion. However, many of these proposals focus on extending existing centralized systems that are very difficult to scale, and offer poor performance in geo-replicated scenarios. We propose a scalable, highly available, and geo-replicated privacy-preserving key value store. A system that provides its users with secure data types meant to be replicated, along with a rich query interface with configurable privacy that enables one to issue secure and somewhat complex queries. We accompany our proposal with an implementation of a privacy-preserving client library for AntidoteDB, a geo-replicated key-value store. We also extend the AntidoteDB’s query language interface by adding support for secure SQL-like queries with configurable privacy. Experimental evaluations show that our proposals offer a feasible solution to practical applications that wish to improve their privacy and confidentiality

Privacy Preserving Public Auditing and Data Integrity for Secure Cloud Storage Using Third Party Auditor

Using cloud services, anyone can remotely store their data and can have the on-demand high quality applications and services from a shared pool of computing resources, without the burden of local data storage and maintenance. Cloud is a commonplace for storing data as well as sharing of that data. However, preserving the privacy and maintaining integrity of data during public auditing remains to be an open challenge. In this paper, we introducing a third party auditor (TPA), which will keep track of all the files along with their integrity. The task of TPA is to verify the data, so that the user will be worry-free. Verification of data is done on the aggregate authenticators sent by the user and Cloud Service Provider (CSP). For this, we propose a secure cloud storage system which supports privacy-preserving public auditing and blockless data verification over the cloud

Efficient Multi - Keyword Ranked Search over Encrypted Cloud Computing

Cloud computing allow customer to store their data on remote site so it reduce burden on local complex data storing. But before storing sensitive data it can encrypted and this can overcome plaintext keyword search.AS large number of user and data on cloud and for search on that data allow multi keyword search also provide result similarity ranking for effective retrieval of data. From number of multi-keyword semantics to identify similarity between search query and data highly efficient rule of coordinate matching, i.e., as many matches as possible, and then use inner data similarity for quantitatively similarity measure. In this system, we define and solve the challenging problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE),and establish a set of strict privacy requirements for such a secure cloud data utilization system to be implemented in real. We first propose basic idea of different privacy preserving multi-keyword search technique along with search on data that store on cloud in encrypted form and maintaining the integrity of rank order in search result and the cloud server is untrusted. .By hiding the user’s identity, the confidentiality of user’s data is maintaine