Secure Cloud Computing

Model combination in classification, density
estimation and clustering and forecasting has
become a promising research area where better
results are obtained compared to using single
models. Bayesian inference offers two
approaches for combining models; Bayesian
model averaging (BMA), and Supra-Bayesian
classifier combination (SBCC). In this paper
we briefly overview the BMA, show its
drawbacks, and propose a novel idea of
Bayesian data/model averaging. Then, the
SBCC technique is explained, and its
implementation used in this work is presented.
A novel approach is proposed where input
space is clustered before applying the SBCC.
A 2-dimensional, 2-class, 4-experts problem is
used to test the SBCC and SBCC-clustering
algorithms, where better results are obtained
over 300 runs for the data set, compared to an
averaging technique, where the 4 experts’
performance is averaged.

With the expanding interest for constant applications in the Wireless Senor Network (WSN), ongoing discriminating occasions suspect a proficient quality-of-service (QoS) based Routing for information conveyance from the Network foundation. Outlining such QoS based Routing convention to meet the unwavering quality and postponement certification of discriminating occasions while protecting the vitality effectiveness is a testing assignment. Impressive exploration has been centred on creating hearty vitality productive QoS based Routing conventions. In this paper, we tended to a remote crossover arrange that incorporates a mobile wireless ad hoc network (MANET) and a wireless infrastructure network has been turned out to be a finer option for the cutting edge remote Networks. By straight forwardly embracing asset reservation-based QoS Routing for MANETs, half breeds Networks inherit invalid reservation and race condition issues in MANETs. Instructions to ensure the QoS in cross breed Networks remains an open issue. In this paper, we propose QoS-Oriented Distributed routing protocol (QOD) to upgrade the QoS help capacity of hybrid Networks.

Social network is a group of individuals with diverse social interactions amongst them. The network large scale and distributed due to Quantitative analysis of networks is need of and in turn the society. Clustering helps us to group people with similar characteristics dense social networks. We have considered similarity measures for statistical When a social network is represented as a graph with members as nodes and their relation as edges, graph mining would be suitable for statistical analysis. We have chosen academic social network nodesto simplify network analysis. similaritybetween unstructured data elements extracted from social network.

Security has been considered as the key concern of adopting cloud computing. This could be ascribed to the customers’ lack of control over their confidential data once in the cloud and to the absence of trust in cloud providers. Unfortunately, the research efforts in the area of cloud security have not yet succeeded to give the required ”peace of mind” to cloud customers. Therefore, in an attempt to figure out the main loopholes in cloud security, this paper questions the  fundamental principles of the security approaches in the cloud. The paper highlights two main drawbacks; the first is due to a potential incompatibility among security measures, while the other is due to the focus on securing the  infrastructure rather than the data itself. Therefore, the paper advocates the need for integrating information-centric security approaches, and hence presents a novel security framework that adopts both infrastructure and
information-centric security services.

Social network is a group of individuals with diverse social interactions amongst them. The network large scale and distributed due to Quantitative analysis of networks is need of and in turn the society. Clustering helps us to group people with similar characteristics dense social networks. We have considered similarity measures for statistical When a social network is represented as a graph with members as nodes and their relation as edges, graph mining would be suitable for statistical analysis. We have chosen academic social network nodesto simplify network analysis. similaritybetween unstructured data elements extracted from social network.

The multihop routing in wireless sensor networks (WSNs) offers little protection against identity deception through replaying routing information. An adversary can exploit this defect to launch various harmful or even devastating attacks against the routing protocols, including sinkhole attacks, wormhole attacks, and Sybil attacks. The situation is further aggravated by mobile and harsh network conditions. Traditional cryptographic techniques or efforts at developing trustaware routing protocols do not effectively address this severe problem. To secure the WSNs against adversaries misdirecting the multihop routing, we have designed and implemented TARF, a robust trust-aware routing framework for dynamic WSNs. Without tight time synchronization or known geographic information, TARF provides trustworthy and energyefficient route. Most importantly, TARF proves effective against those harmful attacks developed out of identity deception; the resilience of TARF is verified through extensive evaluation with both simulation and empirical experiments on large-scale WSNs under various scenarios including mobile and RF-shielding network conditions. Further, we have implemented a low-overhead TARF module in TinyOS; as demonstrated, this implementation can be incorporated into existing routing protocols with the least effort. Based on TARF, we also demonstrated a proof-of-concept mobile target detection application that functions well against an antidetection mechanism.

Photo sharing is an attractive feature which popularizes Online Social Networks (OSNs). Unfortunately, it may leak users' privacy if they are allowed to post, comment, and tag a photo freely.we attempt to address this issue and study the scenario when a user shares a photo containing individuals other than himself/herself (termed co-photo for short). To prevent possible privacy leakage of a photo, we design a mechanism to enable each individual in a photo be aware of the posting activity and participate in the decision making on the photo posting. For this purpose, we need an efficient facial recognition (FR) system that can recognize everyone in the photo. However, more demanding privacy setting may limit the number of the photos publicly available to train the FR system. To deal with this dilemma, our mechanism attempts to utilize users' private photos to design a personalized FR system specifically trained to differentiate possible photo co-owners without leaking their privacy. We also develop a distributed consensusbased method to reduce the computational complexity and protect the private training set. We show that our system is superior to other possible approaches in terms of recognition ratio and efficiency.

Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications.

Broadcast encryption (BE) schemes allow a sender to securely broadcast to any subset of members but require a trusted party to distribute decryption keys. Group key agreement (GKA) protocols enable a group of members to negotiate a common encryption key via open networks so that only the group members can decrypt the ciphertexts encrypted under the shared encryption key, but a sender cannot exclude any particular member from decrypting the ciphertexts. In this paper, we bridge these two notions with a hybrid primitive referred to as contributory broadcast encryption (ConBE). In this new primitive, a group of members negotiate a common public encryption key while each member holds a decryption key. A sender seeing the public group encryption key can limit the decryption to a subset of members of his choice.

A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, …, lq)-diversity and (t1, …, tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: an anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer's objective. Our proposed method was experimentally evaluated using real data sets.

Model combination in classification, density estimation and clustering and forecasting has become a promising research area where better results are obtained compared to using single models. Bayesian inference offers two approaches for combining models; Bayesian model averaging (BMA), and Supra-Bayesian classifier combination (SBCC). In this paper we briefly overview the BMA, show its drawbacks, and propose a novel idea of Bayesian data/model averaging. Then, the SBCC technique is explained, and its implementation used in this work is presented. A novel approach is proposed where input space is clustered before applying the SBCC. A 2-dimensional, 2-class, 4-experts problem is used to test the SBCC and SBCC-clustering algorithms, where better results are obtained over 300 runs for the data set, compared to an averaging technique, where the 4 experts’ performance is averaged.

Understanding the spreading dynamics of computer viruses (worms, attacks) is an important research problem, and has received much attention from the communities of both computer security and statistical physics. However, previous studies have mainly focused on single-virus spreading dynamics. In this paper, we study multi-virus spreading dynamics, where multiple viruses attempt to infect computers while possibly combating against each other because, for example, they are controlled by multiple bot masters. Specifically, we propose and analyze a general model (and its two special cases) of multi-virus spreading dynamics in arbitrary networks (i.e., we do not make any restriction on network topologies), where the viruses may or may not core side on computers. Our model offers analytical results for addressing questions such as: What are the sufficient conditions (also known as epidemic thresholds) under which the multiple viruses will die out? What if some viruses can "rob" others? What characteristics does the multivirus epidemic dynamics exhibit when the viruses are (approximately) equally powerful? The analytical results make a fundamental connection between two types of factors: defense capability and network connectivity. This allows us to draw various insights that can be used to guide security defense.

Cloud computing is quickly becoming the platform of choice for many web services. Virtualization is the key underlying technology enabling cloud providers to host services for a large number of customers. Unfortunately, virtualization software is large, complex, and has a considerable attack

Tons of online user behavior data are being generated every day on the booming and ubiquitous Internet. Growing effortshave been devoted to mining the abundant behavior data to extract valuable information for research purposes or business interests.However, online users' privacy is thus under the risk of being exposed to third-parties. The last decade has witnessed a body ofresearch works trying to perform data aggregation in a privacy-preserving way. Most of existing methods guarantee strong privacyprotection yet at the cost of very limited aggregation operations, such as allowing only summation, which hardly satisfies the need ofbehavior analysis. In this paper, we propose a scheme PPSA, which encrypts users' sensitive data to prevent privacy disclosure fromboth outside analysts and the aggregation service provider, and fully supports selective aggregate functions for online user behavioranalysis while guaranteeing differential privacy. We have implemented our method and evaluated its performance using a trace-drivenevaluation based on a real online behavior dataset. Experiment results show that our scheme effectively supports both overallaggregate queries and various selective aggregate queries with acceptable computation and communication overheads. EXISTING SYSTEM:  Jung et al. proposed a system that can perform multivariate polynomial evaluation. Unfortunately, they still do not support selection. However, selective aggregation is one of the most important operations for queries on databases. It can be used to tell the difference