Data Sharing and Access Using Aggregate Key Concept

Cloud Storage is a capacity of information online in the cloud, which is available from different and associated assets. Distributed storage can provide high availability and consistent quality, reliable assurance, debacle free restoration, and reduced expense. Distributed storage has imperative usefulness, i.e., safely, proficiently, adaptably offering information to others. Data privacy is essential in the cloud to ensure that the user’s identity is not leaked to unauthorized persons. Using the cloud, anyone can share and store the data, as much as they want. To share the data in a secure way, cryptography is very useful. By using different encryption techniques, a user can store data in the cloud. Encryption and decryption keys are created for unique data that the user provides. Only a particular set of decryption keys are shared so that the data can be decrypted. A public–key encryption system which is called a Key-Aggregate cryptosystem (KAC) is presented. This system produces constant size ciphertexts. Any arrangement of secret keys can be aggregated and make them into a single key, which has the same power of the keys that are being used. This total key can then be sent to the others for decoding of a ciphertext set and remaining encoded documents outside the set stays private. The project presented in this paper is an implementation of the proposed system

PAgIoT - Privacy-preserving aggregation protocol for internet of things

Modern society highly relies on the use of cyberspace to perform a huge variety of activities, such as social networking or e-commerce, and new technologies are continuously emerging. As such, computer systems may store a huge amount of information, which makes data analysis and storage a challenge. Information aggregation and correlation are two basic mechanisms to reduce the problem size, for example by filtering out redundant data or grouping similar one. These processes require high processing capabilities, and thus their application in Internet of Things (IoT) scenarios is not straightforward due to resource constraints. Furthermore, privacy issues may arise when the data at stake is personal. In this paper we propose PAgIoT, a Privacy-preserving Aggregation protocol suitable for IoT settings. It enables multi-attribute aggregation for groups of entities while allowing for privacy-preserving value correlation. Results show that PAgIoT is resistant to security attacks, it outperforms existing proposals that provide with the same security features, and it is feasible in resource-constrained devices and for aggregation of up to 10 attributes in big networks.This work was partially supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You) and the CAM grant S2013/ICE-3095 CIBERDINE-CM (CIBERDINE: Cybersecurity, Data, and Risks) funded by the Autonomous Community of Madrid and co-funded by European funds

Privacy Preserving Distributed Data Mining

Privacy preserving distributed data mining aims to design secure protocols which allow multiple parties to conduct collaborative data mining while protecting the data privacy. My research focuses on the design and implementation of privacy preserving two-party protocols based on homomorphic encryption. I present new results in this area, including new secure protocols for basic operations and two fundamental privacy preserving data mining protocols. I propose a number of secure protocols for basic operations in the additive secret-sharing scheme based on homomorphic encryption. I derive a basic relationship between a secret number and its shares, with which we develop efficient secure comparison and secure division with public divisor protocols. I also design a secure inverse square root protocol based on Newton\u27s iterative method and hence propose a solution for the secure square root problem. In addition, we propose a secure exponential protocol based on Taylor series expansions. All these protocols are implemented using secure multiplication and can be used to develop privacy preserving distributed data mining protocols. In particular, I develop efficient privacy preserving protocols for two fundamental data mining tasks: multiple linear regression and EM clustering. Both protocols work for arbitrarily partitioned datasets. The two-party privacy preserving linear regression protocol is provably secure in the semi-honest model, and the EM clustering protocol discloses only the number of iterations. I provide a proof-of-concept implementation of these protocols in C++, based on the Paillier cryptosystem

A Hybrid Approach to Privacy-Preserving Federated Learning

Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or differential privacy which can lead to low accuracy given a large number of parties with relatively small amounts of data each. In this paper, we present an alternative approach that utilizes both differential privacy and SMC to balance these trade-offs. Combining differential privacy with secure multiparty computation enables us to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while maintaining a pre-defined rate of trust. Our system is therefore a scalable approach that protects against inference threats and produces models with high accuracy. Additionally, our system can be used to train a variety of machine learning models, which we validate with experimental results on 3 different machine learning algorithms. Our experiments demonstrate that our approach out-performs state of the art solutions

Revocable Key-Aggregate Cryptosystem for Data Sharing in Cloud

With the rapid development of network and storage technology, cloud storage has become a new service mode, while data sharing and user revocation are important functions in the cloud storage. Therefore, according to the characteristics of cloud storage, a revocable key-aggregate encryption scheme is put forward based on subset-cover framework. The proposed scheme not only has the key-aggregate characteristics, which greatly simplifies the user’s key management, but also can revoke user access permissions, realizing the flexible and effective access control. When user revocation occurs, it allows cloud server to update the ciphertext so that revoked users can not have access to the new ciphertext, while nonrevoked users do not need to update their private keys. In addition, a verification mechanism is provided in the proposed scheme, which can verify the updated ciphertext and ensure that the user revocation is performed correctly. Compared with the existing schemes, this scheme can not only reduce the cost of key management and storage, but also realize user revocation and achieve user’s access control efficiently. Finally, the proposed scheme can be proved to be selective chosen-plaintext security in the standard model

Still Wrong Use of Pairings in Cryptography

Several pairing-based cryptographic protocols are recently proposed with a wide variety of new novel applications including the ones in emerging technologies like cloud computing, internet of things (IoT), e-health systems and wearable technologies. There have been however a wide range of incorrect use of these primitives. The paper of Galbraith, Paterson, and Smart (2006) pointed out most of the issues related to the incorrect use of pairing-based cryptography. However, we noticed that some recently proposed applications still do not use these primitives correctly. This leads to unrealizable, insecure or too inefficient designs of pairing-based protocols. We observed that one reason is not being aware of the recent advancements on solving the discrete logarithm problems in some groups. The main purpose of this article is to give an understandable, informative, and the most up-to-date criteria for the correct use of pairing-based cryptography. We thereby deliberately avoid most of the technical details and rather give special emphasis on the importance of the correct use of bilinear maps by realizing secure cryptographic protocols. We list a collection of some recent papers having wrong security assumptions or realizability/efficiency issues. Finally, we give a compact and an up-to-date recipe of the correct use of pairings.Comment: 25 page

Empirical Analysis of Privacy Preservation Models for Cyber Physical Deployments from a Pragmatic Perspective

The difficulty of privacy protection in cyber-physical installations encompasses several sectors and calls for methods like encryption, hashing, secure routing, obfuscation, and data exchange, among others. To create a privacy preservation model for cyber physical deployments, it is advised that data privacy, location privacy, temporal privacy, node privacy, route privacy, and other types of privacy be taken into account. Consideration must also be given to other types of privacy, such as temporal privacy. The computationally challenging process of incorporating these models into any wireless network also affects quality of service (QoS) variables including end-to-end latency, throughput, energy use, and packet delivery ratio. The best privacy models must be used by network designers and should have the least negative influence on these quality-of-service characteristics. The designers used common privacy models for the goal of protecting cyber-physical infrastructure in order to achieve this. The limitations of these installations' interconnection and interface-ability are not taken into account in this. As a result, even while network security has increased, the network's overall quality of service has dropped. The many state-of-the-art methods for preserving privacy in cyber-physical deployments without compromising their performance in terms of quality of service are examined and analyzed in this research. Lowering the likelihood that such circumstances might arise is the aim of this investigation and review. These models are rated according to how much privacy they provide, how long it takes from start to finish to transfer data, how much energy they use, and how fast their networks are. In order to maximize privacy while maintaining a high degree of service performance, the comparison will assist network designers and researchers in selecting the optimal models for their particular deployments. Additionally, the author of this book offers a variety of tactics that, when used together, might improve each reader's performance. This study also provides a range of tried-and-true machine learning approaches that networks may take into account and examine in order to enhance their privacy performance

Optimizing Key Management within a Crypto-System using Aggregate Keys

Sharing data with peers is an important functionality in cloud storage. This is a study and analysis of secure, efficient, and flexible sharing of data with other users in cloud storage. The new public key encryptions which produce constant-size ciphertexts in such a way that effective delegation of decryption rights given to any set of ciphertexts are described in this paper. The novelty of the mechanism is that someone can aggregate any number of secret keys and turn them into a small single key, but combine the power of all the keys being grouped. To describe, in other words, the secret key holder could release a constant-size aggregate key for more flexible choices of ciphertext set in cloud storage, but different encrypted files outside of the set remain confidential. The aggregate compact key can be sent to others with ease or saved in a smart card with very less secure storage. In this paper, we discuss various such mechanisms and demonstrate the topic with a project. Some of the papers written by other authors in the area are analyzed in this paper. The project in this paper is a partial implementation of the proposed Crypto System