Biblio
When clients interact with a cloud-based service, they expect certain levels of quality of service guarantees. These are expressed as security and privacy policies, interaction authorization policies, and service performance policies among others. The main security challenge in a cloud-based service environment, typically modeled using service-oriented architecture (SOA), is that it is difficult to trust all services in a service composition. In addition, the details of the services involved in an end-to-end service invocation chain are usually not exposed to the clients. The complexity of the SOA services and multi-tenancy in the cloud environment leads to a large attack surface. In this paper we propose a novel approach for end-to-end security and privacy in cloud-based service orchestrations, which uses a service activity monitor to audit activities of services in a domain. The service monitor intercepts interactions between a client and services, as well as among services, and provides a pluggable interface for different modules to analyze service interactions and make dynamic decisions based on security policies defined over the service domain. Experiments with a real-world service composition scenario demonstrate that the overhead of monitoring is acceptable for real-time operation of Web services.
In this paper, we present AnomalyDetect, an approach for detecting anomalies in cloud services. A cloud service consists of a set of interacting applications/processes running on one or more interconnected virtual machines. AnomalyDetect uses the Kalman Filter as the basis for predicting the states of virtual machines running cloud services. It uses the cloud service's virtual machine historical data to forecast potential anomalies. AnomalyDetect has been integrated with the AutoMigrate framework and serves as the means for detecting anomalies to automatically trigger live migration of cloud services to preserve their availability. AutoMigrate is a framework for developing intelligent systems that can monitor and migrate cloud services to maximize their availability in case of cloud disruption. We conducted a number of experiments to analyze the performance of the proposed AnomalyDetect approach. The experimental results highlight the feasibility of AnomalyDetect as an approach to autonomic cloud availability.
A major issue that arises from mass visual media distribution in modern video sharing, social media and cloud services, is the issue of privacy. Malicious users can use these services to track the actions of certain individuals and/or groups thus violating their privacy. As a result the need to hinder automatic facial image identification in images and videos arises. In this paper we propose a method for de-identifying facial images. Contrary to most de-identification methods, this method manipulates facial images so that humans can still recognize the individual or individuals in an image or video frame, but at the same time common automatic identification algorithms fail to do so. This is achieved by projecting the facial images on a hypersphere. From the conducted experiments it can be verified that this method is effective in reducing the classification accuracy under 10%. Furthermore, in the resulting images the subject can be identified by human viewers.
Many cloud security complexities can be concerned as a result of its open system architecture. One of these complexities is multi-tenancy security issue. This paper discusses and addresses the most common public cloud security complexities focusing on Multi-Tenancy security issue. Multi-tenancy is one of the most important security challenges faced by public cloud services providers. Therefore, this paper presents a secure multi-tenancy architecture using authorization model Based on AAAS protocol. By utilizing cloud infrastructure, access control can be provided to various cloud information and services by our suggested authorization system. Each business can offer several cloud services. These cloud services can cooperate with other services which can be related to the same organization or different one. Moreover, these cooperation agreements are supported by our suggested system.
In the era of Cloud and Social Networks, mobile devices exhibit much more powerful abilities for big media data storage and sharing. However, many users are still reluctant to share/store their data via clouds due to the potential leakage of confidential or private information. Although some cloud services provide storage encryption and access protection, privacy risks are still high since the protection is not always adequately conducted from end-to-end. Most customers are aware of the danger of letting data control out of their hands, e.g., Storing them to YouTube, Flickr, Facebook, Google+. Because of substantial practical and business needs, existing cloud services are restricted to the desired formats, e.g., Video and photo, without allowing arbitrary encrypted data. In this paper, we propose a format-compliant end-to-end privacy-preserving scheme for media sharing/storage issues with considerations for big data, clouds, and mobility. To realize efficient encryption for big media data, we jointly achieve format-compliant, compression-independent and correlation-preserving via multi-channel chained solutions under the guideline of Markov cipher. The encryption and decryption process is integrated into an image/video filter via GPU Shader for display-to-display full encryption. The proposed scheme makes big media data sharing/storage safer and easier in the clouds.
Cloud computing is one of the emerging computing technology where costs are directly proportional to usage and demand. The advantages of this technology are the reasons of security and privacy problems. The data belongs to the users are stored in some cloud servers which is not under their own control. So the cloud services are required to authenticate the user. In general, most of the cloud authentication algorithms do not provide anonymity of the users. The cloud provider can track the users easily. The privacy and authenticity are two critical issues of cloud security. In this paper, we propose a secure anonymous authentication method for cloud services using identity based group signature which allows the cloud users to prove that they have privilege to access the data without revealing their identities.
Efficient and secure search on encrypted data is an important problem in computer science. Users having large amount of data or information in multiple documents face problems with their storage and security. Cloud services have also become popular due to reduction in cost of storage and flexibility of use. But there is risk of data loss, misuse and theft. Reliability and security of data stored in the cloud is a matter of concern, specifically for critical applications and ones for which security and privacy of the data is important. Cryptographic techniques provide solutions for preserving the confidentiality of data but make the data unusable for many applications. In this paper we report a novel approach to securely store the data on a remote location and perform search in constant time without the need for decryption of documents. We use bloom filters to perform simple as well advanced search operations like case sensitive search, sentence search and approximate search.
With the rapid increase in cloud services collecting and using user data to offer personalized experiences, ensuring that these services comply with their privacy policies has become a business imperative for building user trust. However, most compliance efforts in industry today rely on manual review processes and audits designed to safeguard user data, and therefore are resource intensive and lack coverage. In this paper, we present our experience building and operating a system to automate privacy policy compliance checking in Bing. Central to the design of the system are (a) Legal ease-a language that allows specification of privacy policies that impose restrictions on how user data is handled, and (b) Grok-a data inventory for Map-Reduce-like big data systems that tracks how user data flows among programs. Grok maps code-level schema elements to data types in Legal ease, in essence, annotating existing programs with information flow types with minimal human input. Compliance checking is thus reduced to information flow analysis of Big Data systems. The system, bootstrapped by a small team, checks compliance daily of millions of lines of ever-changing source code written by several thousand developers.
The goal of this letter is to explore the extent to which the vulnerabilities plaguing the Internet, particularly susceptibility to distributed denial-of-service (DDoS) attacks, impact the Cloud. DDoS has been known to disrupt Cloud services, but could it do worse by permanently damaging server and switch hardware? Services are hosted in data centers with thousands of servers generating large amounts of heat. Heating, ventilation, and air-conditioning (HVAC) systems prevent server downtime due to overheating. These are remotely managed using network management protocols that are susceptible to network attacks. Recently, Cloud providers have experienced outages due to HVAC malfunctions. Our contributions include a network simulation to study the feasibility of such an attack motivated by our experiences of such a security incident in a real data center. It demonstrates how a network simulator can study the interplay of the communication and thermal properties of a network and help prevent the Cloud provider's worst nightmare: meltdown of the data center as a result of a DDoS attack.