AccountTrade: Accountability Against Dishonest Big Data Buyers and Sellers

AccountTrade: Accountability Against Dishonest Big Data Buyers and Sellers

ABSTRACT:

In this paper, a set of accountable protocols denoted as AccountTrade is proposed for big data trading among dishonest consumers. For achieving secure the big data trading environment, AccountTrade achieves book-keeping ability and accountability against dishonest consumers throughout the trading (i.e., buying and selling) of datasets. We investigate the consumers’ responsibilities in the dataset trading, then we design AccountTrade to achieve accountability against dishonest consumers that are likely to deviate from the responsibilities. Specifically, a uniqueness index is defined and proposed, which is a new rigorous measurement of the data uniqueness for this purpose. Furthermore, several accountable trading protocols are presented to enable data brokers to blame the misbehaving entities when misbehavior is detected. The accountability of AccountTrade is formally defined, proved, and evaluated by an automatic verification tool as well as extensive simulation with real-world datasets. Our evaluation shows that AccountTrade incurs at most 10KB storage overhead per file, and it is capable of 8-1000 concurrent data upload requests per server.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Taeho Jung, Member, IEEE, Xiang-Yang Li, Fellow, IEEE, Wenchao Huang, Zhongying Qiao, Jianwei Qian, Linlin Chen, Junze Han, Jiahui Hou, “AccountTrade: Accountability Against Dishonest Big Data Buyers and Sellers”, IEEE Transactions on Information Forensics and Security, 2018.

A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds

A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds

ABSTRACT:

Due to the complexity and volume, outsourcing ciphertexts to a cloud is deemed to be one of the most effective approaches for big data storage and access. Nevertheless, verifying the access legitimacy of a user and securely updating a ciphertext in the cloud based on a new access policy designated by the data owner are two critical challenges to make cloud-based big data storage practical and effective. Traditional approaches either completely ignore the issue of access policy update or delegate the update to a third party authority; but in practice, access policy update is important for enhancing security and dealing with the dynamism caused by user join and leave activities. In this paper, we propose a secure and verifiable access control scheme based on the NTRU cryptosystem for big data storage in clouds. We first propose a new NTRU decryption algorithm to overcome the decryption failures of the original NTRU, and then detail our scheme and analyze its correctness, security strengths, and computational efficiency. Our scheme allows the cloud server to efficiently update the ciphertext when a new access policy is specified by the data owner, who is also able to validate the update to counter against cheating behaviors of the cloud. It also enables (i) the data owner and eligible users to effectively verify the legitimacy of a user for accessing the data, and (ii) a user to validate the information provided by other users for correct plaintext recovery. Rigorous analysis indicates that our scheme can prevent eligible users from cheating and resist various attacks such as the collusion attack.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Chunqiang Hu, Member, IEEE, Wei Li, Member, IEEE, Xiuzhen Cheng, Fellow, IEEE Jiguo Yu, Member, IEEE, Shengling Wang, Member, IEEE, and Rongfang Bie, Member, IEEE, “A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds”, IEEE Transactions on Big Data, 2018.

Enabling Probabilistic Differential Privacy Protection for Location Recommendations

Enabling Probabilistic Differential Privacy Protection for Location Recommendations

ABSTRACT:

The sequential pattern in the human movement is one of the most important aspects for location recommendations in geosocial networks. Existing location recommenders have to access users’ raw check-in data to mine their sequential patterns that raises serious location privacy breaches. In this paper, we propose a new Privacy-preserving LOcation REcommendation framework (PLORE) to address this privacy challenge. First, we employ the nth-order additive Markov chain to exploit users’ sequential patterns for location recommendations. Further, we contrive the probabilistic differential privacy mechanism to reach a good trade-off between high recommendation accuracy and strict location privacy protection. Finally, we conduct extensive experiments to evaluate the performance of PLORE using three large-scale real-world data sets. Extensive experimental results show that PLORE provides efficient and highly accurate location recommendations, and guarantees strict privacy protection for user check-in data in geosocial networks.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Jia-Dong Zhang, and Chi-Yin Chow, Senior Member, IEEE, “Enabling Probabilistic Differential Privacy Protection for Location Recommendations”, IEEE Transactions on Services Computing, 2018

 

A Developer Centered Bug Prediction Model

A Developer Centered Bug Prediction Model

ABSTRACT:

Several techniques have been proposed to accurately predict software defects. These techniques generally exploit characteristics of the code artefacts (e.g., size, complexity, etc.) and/or of the process adopted during their development and maintenance (e.g., the number of developers working on a component) to spot out components likely containing bugs. While these bug prediction models achieve good levels of accuracy, they mostly ignore the major role played by human-related factors in the introduction of bugs. Previous studies have demonstrated that focused developers are less prone to introduce defects than non-focused developers. According to this observation, software components changed by focused developers should also be less error prone than components changed by less focused developers. We capture this observation by measuring the scattering of changes performed by developers working on a component and use this information to build a bug prediction model. Such a model has been evaluated on 26 systems and compared with four competitive techniques. The achieved results show the superiority of our model, and its high complementarity with respect to predictors commonly used in the literature. Based on this result, we also show the results of a “hybrid” prediction model combining our predictors with the existing ones.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Dario Di Nucci, Fabio Palomba, Giuseppe De Rosa, “A Developer Centered Bug Prediction Model”, IEEE Transactions on Software Engineering, 2018.

Scalable Access Control For Privacy-Aware Media Sharing

Scalable Access Control For Privacy-Aware Media Sharing

ABSTRACT:

The prevalence of social networks has made it easier than ever for users to share their photos, videos, and other media content with anybody from anywhere. However, the easy access of user-generated media content also brings about privacy concerns. Traditional access control mechanisms, where a single access policy is made for a specific piece of content, cannot satisfy the user privacy requirements in large-scale media sharing systems. Instead, configuring multiple levels of access privileges for the shared media content is desired. On one hand, it conforms to the principle of social networks in information propagation. On the other hand, it accords with the diverse and complex social relationship among social network users. In this paper, we propose a scalable media access control (SMAC) system to enable such a configuration in a secure and efficient manner. The proposed SMAC system is empowered by the scalable ciphertext policy attribute-based encryption (SCP-ABE) algorithm as well as a comprehensive key management scheme. We provide formal security proof to prove the security of the proposed SMAC system. Additionally, we conduct intensive experiments on mobile devices to demonstrate its efficiency.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Changsha Ma, Zhisheng Yan, and Chang Wen Chen, Fellow, IEEE, “Scalable Access Control For Privacy-Aware Media Sharing”, IEEE Transactions on Multimedia, 2018.

New Framework of Reversible Data Hiding in Encrypted JPEG Bitstreams

New Framework of Reversible Data Hiding in Encrypted JPEG Bitstreams

ABSTRACT:

This paper proposes a novel framework of reversible data hiding in encrypted JPEG bitstream. We first provide a JPEG encryption algorithm to encipher a JPEG image to a smaller size and keep the format compliant to JPEG decoders. After an image owner uploads the encrypted JPEG bitstreams to cloud storage, the server embeds additional messages into the ciphertext to construct a marked encrypted JPEG bitstream. During data hiding, we propose a combined embedding algorithm including two stages, the Huffman code mapping and the ordered histogram shifting. The embedding procedure is reversible. When an authorized user requires a downloading operation, the server extracts additional messages from the marked encrypted JPEG bitstream and recovers the original encrypted bit-stream losslessly. After downloading, the user obtains the original JPEG bitstream by a direct decryption. The proposed framework out-performs previous works on RDH-EI. First, since the tasks of data embedding/extraction and bitstream recovery are all accomplished by the server, the image owner and the authorized user are required to implement no extra operations except JPEG encryption or decryption. Second, the embedding payload is larger than state-of-the-art works.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Zhenxing Qian, Member, IEEE, Haisheng Xu, Xiangyang Luo, Xinpeng Zhang, Member, IEEE, “New Framework of Reversible Data Hiding in Encrypted JPEG Bitstreams”, IEEE Transactions on Circuits and Systems for Video Technology, 2018.

Efficient Rectification of Distorted Fingerprints

Efficient Rectification of Distorted Fingerprints

ABSTRACT:

Recently, distortion rectification based on a single fingerprint image has been shown to be able to significantly improve the recognition rate of distorted fingerprints. However, the computational complexity of such method is too high to be useful in practice. In this paper, we propose a novel method for the rectification of distorted fingerprints, whose speed is over 30 times faster than the existing method. This significant speedup is due to a Hough forest based two-step fingerprint pose estimation algorithm and a support vector regressor based fingerprint distortion field estimation algorithm. Experimental results on public domain databases show that our method can achieve as good rectification performance as the existing method but meanwhile is significantly faster.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Shan Gu, Student Member, IEEE, Jianjiang Feng, Member, IEEE, Jiwen Lu, Senior Member, IEEE, and Jie Zhou, Senior Member, IEEE, “Efficient Rectification of Distorted Fingerprints”, IEEE Transactions on Information Forensics and Security, 2018.

Cloud-based Fine-grained Health Information Access Control Framework for Lightweight IoT Devices with Dynamic Auditing and Attribute Revocation

Cloud-based Fine-grained Health Information Access Control Framework for Lightweight IoT Devices with Dynamic Auditing and Attribute Revocation

ABSTRACT:

The eHealth trend has spread globally. Internet of Things (IoT) devices for medical service and pervasive Personal Health Information (PHI) systems play important roles in the eHealth environment. A cloud-based PHI system appears promising but raises privacy and information security concerns. We propose a cloud-based fine-grained health information access control framework for lightweight IoT devices with data dynamics auditing and attribute revocation functions. Only symmetric cryptographyis required for IoT devices, such as wireless body sensors. A variant of cipher text-policy attribute-based encryption, dual encryption, and Merkle hash trees are used to support fine-grained access control, efficient dynamic data auditing, batch auditing, and attribute revocation. Moreover, the proposed scheme also defines and handles the cloud reciprocity problem wherein cloud service providers can help each other avoid fines resulting from data loss. Security analysis and performance comparisons show that the proposed scheme is an excellent candidate for a cloud-based PHI system.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Lo-Yao Yeh, Pei-Yu Chiang, Yi-Lang Tsai, and Jiun-Long Huang, IEEE Member, “Cloud-based Fine-grained Health Information Access Control Framework for Lightweight IoT Devices with Dynamic Auditing and Attribute Revocation”,   IEEE Transactions on Cloud Computing, 2018.

Searchable Encryption over Feature-Rich Data

Searchable Encryption over Feature-Rich Data

ABSTRACT:

Storage services allow data owners to store their huge amount of potentially sensitive data, such as audios, images, and videos, on remote cloud servers in encrypted form. To enable retrieval of encrypted files of interest, many searchable symmetric encryption (SSE) schemes have been proposed. However, most existing SSE solutions construct indexes based on keyword-file pairs and focus on boolean expressions of exact keyword matches. Moreover, most dynamic SSE solutions cannot achieve forward privacy and reveal unnecessary information when updating the encrypted databases. We tackle the challenge of supporting large-scale similarity search over encrypted feature-rich multimedia data, by considering the search criteria as a high-dimensional feature vector instead of a keyword. Our solutions are built on carefully-designed fuzzy Bloom filters which utilize locality sensitive hashing (LSH) to encode an index associating the file identifiers and feature vectors. Our schemes are proven to be secure against adaptively chosen query attack and forward private in the standard model. We have evaluated the performance of our scheme on various real-world high-dimensional datasets, and achieved a search quality of 99% recall with only a few number of hash tables for LSH. This shows that our index is compact and searching is not only efficient but also accurate.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Qian Wang, Member, IEEE, Meiqi He, Minxin Du, Sherman S. M. Chow, Member, IEEE, Russell W. F. Lai, and Qin Zou Member, IEEE, “Searchable Encryption over Feature-Rich Data”, IEEE Transactions on Dependable and Secure Computing, 2018.

Latent-Data Privacy Preserving With Customized Data Utility for Social Network Data

Latent-Data Privacy Preserving With Customized Data Utility for Social Network Data

ABSTRACT:

Social network data can help with obtaining valuable insight into social behaviors and revealing the underlying benefits. New big data technologies are emerging to make it easier to discover meaningful social information from market analysis to counterterrorism. Unfortunately, both diverse social datasets and big data technologies raise stringent privacy concerns. Adversaries can launch inference attacks to predict sensitive latent information, which is unwilling to be published by social users. Therefore, there is a tradeoff between data benefits and privacy concerns. In this paper, we investigate howto optimize the tradeoff between latent-data privacy and customized data utility. We propose a data sanitization strategy that does not greatly reduce the benefits brought by social network data, while sensitive latent information can still be protected. Even considering powerful adversaries with optimal inference attacks, the proposed data sanitization strategy can still preserve both data benefits and social structure, while guaranteeing optimal latent-data privacy. To the best of our knowledge, this is the first work that preserves both data benefits and social structure simultaneously and combats against powerful adversaries.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB.

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : NET,C#.NET
  • Tool : Visual Studio 2008
  • Database : SQL SERVER 2005

REFERENCE:

Zaobo He, Student Member, IEEE, Zhipeng Cai , Senior Member, IEEE, and Jiguo Yu, “Latent-Data Privacy Preserving With Customized Data Utility for Social Network Data”, IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 67, NO. 1, JANUARY 2018.