Biblio
The Cloud computing in simple terms is storing and accessing data through internet. The data stored in the cloud is managed by cloud service providers. Storing data in cloud saves users time and memory. But once user stores data in cloud, he loses the control over his data. Hence there must be some security issues to be handled to keep users data safely in the cloud. In this work, we projected a secure auditing system using Third Party Auditor (TPA). We used Advanced Encryption Standard (AES) algorithm for encrypting user's data and Secure Hash Algorithm (SHA-2) to compute message digest. The system is executed in Amazon EC2 cloud by creating windows server instance. The results obtained demonstrates that our proposed work is safe and takes a firm time to audit the files.
In the smart grid, residents' electricity usage needs to be periodically measured and reported for the purpose of better energy management. At the same time, real-time collection of residents' electricity consumption may unfavorably incur privacy leakage, which has motivated the research on privacy-preserving aggregation of electricity readings. Most previous studies either rely on a trusted third party (TTP) or suffer from expensive computation. In this paper, we first reveal the privacy flaws of a very recent scheme pursing privacy preservation without relying on the TTP. By presenting concrete attacks, we show that this scheme has failed to meet the design goals. Then, for better privacy protection, we construct a new scheme called PMDA, which utilizes Shamir's secret sharing to allow smart meters to negotiate aggregation parameters in the absence of a TTP. Using only lightweight cryptography, PMDA efficiently supports multi-functional aggregation of the electricity readings, and simultaneously preserves residents' privacy. Theoretical analysis is provided with regard to PMDA's security and efficiency. Moreover, experimental data obtained from a prototype indicates that our proposal is efficient and feasible for practical deployment.
Cloud computing belongs to distributed network technology for computing and storage capabilities purpose. It is a kind of cost-effective technology dedicated to information technology. Using the Internet, the accessibility and retrieving of cloud data have become much more accessible. The service providers can expand the storage space in a cloud environment. Security is well-thought-out to be the essential attribute in a distributed system. Cryptography can be described as a method of securing the data from attackers and eavesdroppers. Third Party Auditor is responsible for the authentication of secret files in cloud system on behalf of the data owner. The data auditability technique allows the user to make the data integrity check using a third party. Cloud computing offers unlimited data space for storage to its users and also serves sharing of data and planned use of heterogeneous resources in distributed systems. This paper describes privacy-preserving enabled public auditing method using cryptographic techniques for low-performance based end devices.
Design of anonymous authentication scheme is one of the most important challenges in Vehicular Ad hoc Networks (VANET). Most of the existing schemes have high computational and communication overhead and they do not meet security requirements. Recently, Azees et al. have introduced an Efficient Anonymous Authentication with Conditional Privacy-Preserving (EAAP) scheme for VANET and claimed that it is secure. In this paper, we show that this protocol is vulnerable against replay attack, impersonation attack and message modification attack. Also, we show that the messages sent by a vehicle are linkable. Therefore, an adversary can easily track the vehicles. In addition, it is shown that vehicles face with some problems when they enter in a new Trusted Authority (TA) range. As a solution, we propose a new authentication protocol which is more secure than EAAP protocol without increasing its computational and communication overhead.
With all data services of cloud, it's not only stored the data, although shared the data among the multiple users or clients, which make doubt in its integrity due to the existence of software/hardware error along with human error too. There is an existence of several mechanisms to allow data holders and public verifiers to precisely, efficiently and effectively audit integrity of cloud data without accessing the whole data from server. After all, public auditing on the integrity of shared data with pervious extant mechanisms will somehow affirm the confidential information and its identity privacy to the public verifiers. In this paper, to achieve the privacy preserving public for auditing, we intended an explanation for TPA using three way handshaking protocol through the Extensible Authentication Protocol (EAP) with liberated encryption standard. Appropriately, from the cloud, we use the VerifyProof execute by TPA to audit to certify. In addition to this mechanism, the identity of each segment in the shared data is kept private from the public verifiers. Moreover, rather than verifying the auditing task one by one, this will capable to perform, the various auditing tasks simultaneously.
It is a challenging problem to preserve the friendly-correlations between individuals when publishing social-network data. To alleviate this problem, uncertain graph has been presented recently. The main idea of uncertain graph is converting an original graph into an uncertain form, where the correlations between individuals is an associated probability. However, the existing methods of uncertain graph lack rigorous guarantees of privacy and rely on the assumption of adversary's knowledge. In this paper we first introduced a general model for constructing uncertain graphs. Then, we proposed an algorithm under the model which is based on differential privacy and made an analysis of algorithm's privacy. Our algorithm provides rigorous guarantees of privacy and against the background knowledge attack. Finally, the algorithm we proposed satisfied differential privacy and showed feasibility in the experiments. And then, we compare our algorithm with (k, ε)-obfuscation algorithm in terms of data utility, the importance of nodes for network in our algorithm is similar to (k, ε)-obfuscation algorithm.
Comparing with the traditional grid, energy internet will collect data widely and connect more broader. The analysis of electrical data use of Non-intrusive Load Monitoring (NILM) can infer user behavior privacy. Consideration both data security and availability is a problem must be addressed. Due to its rigid and provable privacy guarantee, Differential Privacy has proverbially reached and applied to privacy preserving data release and data mining. Because of its high sensitivity, increases the noise directly will led to data unavailable. In this paper, we propose a differentially private mechanism to protect energy internet privacy. Our focus is the aggregated data be released by data owner after added noise in disaggregated data. The theoretically proves and experiments show that our scheme can achieve the purpose of privacy-preserving and data availability.
Smart grid personalized service to improve the accuracy of the grid network query, along with the data security issues worthy of our thinking. How to solve the privacy problem in the smart grid, which is a challenge to the smart grid. As data in the grid becomes more and more important, better algorithms are needed to protect the data. In this paper, we first summarize the influence of k-anonymous algorithm on sensitive attributes in standard identifiers, and then analyze the improved L-diversity algorithm from the perspective of anonymous data privacy and security. Experiments show that the algorithm can protect the data in the smart grid.
Wireless sensor networks are responsible for sensing, gathering and processing the information of the objects in the network coverage area. Basic data fusion technology generally does not provide data privacy protection mechanism, and the privacy protection mechanism in health care, military reconnaissance, smart home and other areas of the application is usually indispensable. In this paper, we consider the privacy, confidentiality, and the accuracy of fusion results, and propose a data fusion algorithm for privacy preserving. This algorithm relies on the characteristics of data fusion, and uses the method of pre-distribution random number in the node to get the privacy protection requirements of the original data. Theoretical analysis shows that the malicious attacker attempts to steal the difficulty of node privacy in PPND algorithm. At the same time in the TOSSIM simulation results also show that, compared with TAG, SMART algorithm, PPND algorithm in the data traffic, the convergence accuracy of the good performance.
Machine learning algorithms based on deep neural networks (NN) have achieved remarkable results and are being extensively used in different domains. On the other hand, with increasing growth of cloud services, several Machine Learning as a Service (MLaaS) are offered where training and deploying machine learning models are performed on cloud providers' infrastructure. However, machine learning algorithms require access to raw data which is often privacy sensitive and can create potential security and privacy risks. To address this issue, we develop new techniques to provide solutions for applying deep neural network algorithms to the encrypted data. In this paper, we show that it is feasible and practical to train neural networks using encrypted data and to make encrypted predictions, and also return the predictions in an encrypted form. We demonstrate applicability of the proposed techniques and evaluate its performance. The empirical results show that it provides accurate privacy-preserving training and classification.
Cloud computing, often referred to as simply “the cloud,” is the delivery of on-demand computing resources; everything from applications to data centers over the Internet. Cloud is used not only for storing data, but also the stored data can be shared by multiple users. Due to this, the integrity of cloud data is subject to doubt. Every time it is not possible for user to download all data and verify integrity, so proposed system contain Third Party Auditor (TPA) to verify the integrity of shared data. During auditing, the shared data is kept private from public verifiers, who are able to verify shared data integrity without downloading or retrieving the entire data file. Group signature is used to preserve identity privacy of group members from third party auditor. Privacy preserving is done to ensure that the TPA cannot derive user's data content from the information collected during the auditing process.
Privacy preserving on data publication has been an important research field over the past few decades. One of the fundamental challenges in privacy preserving data publication is the trade-off problem between privacy and utility of the single and independent data set. However, recent research works have shown that the advanced privacy mechanism, i.e., differential privacy, is vulnerable when multiple data sets are correlated. In this case, the trade-off problem between privacy and utility is evolved into a game problem, in which the payoff of each player is dependent not only on his privacy parameter, but also on his neighbors' privacy parameters. In this paper, we firstly present the definition of correlated differential privacy to evaluate the real privacy level of a single data set influenced by the other data sets. Then, we construct a game model of multiple players, who each publishes the data set sanitized by differential privacy. Next, we analyze the existence and uniqueness of the pure Nash Equilibrium and demonstrate the sufficient conditions in the game. Finally, we refer to a notion, i.e., the price of anarchy, to evaluate efficiency of the pure Nash Equilibrium.
Two mainstream techniques are traditionally used to authorize access to a WiFi network. Small scale networks usually rely on the offline distribution of a WPA/WPA2 static pre-shared secret key (PSK); security hence relies on the fact that this PSK is not leaked by end user, and is not disclosed via dictionary or brute-force attacks. On the other side, Enterprise and large scale networks typically employ online authorization using an 802.1X-based authentication service leveraging a backend online infrastructure (e.g. Radius servers/proxies). In this work, we propose a new mechanism which does not require neither online operation nor backend access control infrastructure, but which does not force us to rely on a static pre-shared secret key. The idea is very simple, yet effective: directly broadcast in the WLAN beacons an encrypted version of the secret key required to access the WLAN network, so that only the users which possess suitable authorization credentials can decrypt and use it. This proposed approach clearly decouples the management of authorization credentials, issued offline to the authorized end users, from the actual secret key used in the WLAN network, which can thus be in principle changed at each new user's access. The solution described in the paper relies on attribute-based encryption, and is designed to be compatible with WPA2 and deployable within standard 802.11 management frames. Since no user identification is required (access control is based on attributes rather than on the user identity), the proposed approach further improves privacy. We demonstrate the feasibility of the proposed solution via a concrete implementation in Linux-based devices and via relevant testing in a real-world experimental setup.
With the growing observed success of big data use, many challenges appeared. Timeless, scalability and privacy are the main problems that researchers attempt to figure out. Privacy preserving is now a highly active domain of research, many works and concepts had seen the light within this theme. One of these concepts is the de-identification techniques. De-identification is a specific area that consists of finding and removing sensitive information either by replacing it, encrypting it or adding a noise to it using several techniques such as cryptography and data mining. In this report, we present a new model of de-identification of textual data using a specific Immune System algorithm known as CLONALG.
Web Service (WS) plays an important role in today's word to provide effective services for humans and these web services are built with the standard of SOAP, WSDL & UDDI. This technology enables various service providers to register and service sender their intelligent agent based privacy preserving modelservices to utilize the service over the internet through pre established networks. Also accessing these services need to be secured and protected from various types of attacks in the network environment. Exchanging data between two applications on a secure channel is a challenging issue in today communication world. Traditional security mechanism such as secured socket layer (SSL), Transport Layer Security (TLS) and Internet Protocol Security (IP Sec) is able to resolve this problem partially, hence this research paper proposes the privacy preserving named as HTTPI to secure the communication more efficiently. This HTTPI protocol satisfies the QoS requirements, such as authentication, authorization, integrity and confidentiality in various levels of the OSI layers. This work also ensures the QoS that covers non functional characteristics like performance (throughput), response time, security, reliability and capacity. This proposed intelligent agent based model results in excellent throughput, good response time and increases the QoS requirements.
The wireless network is become larger than past. So in the recent years the wireless with multiple sinks is more useful. The anonymity and privacy in this network is a challenge now. In this paper, we propose a new method for anonymity in multi sink wireless sensor network. In this method we use layer encryption to provide source and event privacy and we use a label switching routing method to provide sink anonymity in each cluster. A master sink that is a powerful base station is used to connect sinks to each other.