Enhancing Data Security by Making Data Disappear in a P2P Systems

This paper describes the problem of securing data by making it disappear after some time limit, making it impossible for it to be recovered by an unauthorized party. This method is in response to the need to keep the data secured and to protect the privacy of archived data on the servers, Cloud and Peer-to-Peer architectures. Due to the distributed nature of these architectures, it is impossible to destroy the data completely. So, we store the data by applying encryption and then manage the key, which is easier to do as the key is small and it can be hidden in the DHT (Distributed hash table). Even if the keys in the DHT and the encrypted data were compromised, the data would still be secure. This paper describes existing solutions, points to their limitations and suggests improvements with a new secure architecture. We evaluated and executed this architecture on the Java platform and proved that it is more secure than other architectures.Comment: 18 page

Conclave: secure multi-party computation on big data (extended TR)

Secure Multi-Party Computation (MPC) allows mutually distrusting parties to run joint computations without revealing private data. Current MPC algorithms scale poorly with data size, which makes MPC on "big data" prohibitively slow and inhibits its practical use. Many relational analytics queries can maintain MPC's end-to-end security guarantee without using cryptographic MPC techniques for all operations. Conclave is a query compiler that accelerates such queries by transforming them into a combination of data-parallel, local cleartext processing and small MPC steps. When parties trust others with specific subsets of the data, Conclave applies new hybrid MPC-cleartext protocols to run additional steps outside of MPC and improve scalability further. Our Conclave prototype generates code for cleartext processing in Python and Spark, and for secure MPC using the Sharemind and Obliv-C frameworks. Conclave scales to data sets between three and six orders of magnitude larger than state-of-the-art MPC frameworks support on their own. Thanks to its hybrid protocols, Conclave also substantially outperforms SMCQL, the most similar existing system.Comment: Extended technical report for EuroSys 2019 pape

When the Hammer Meets the Nail: Multi-Server PIR for Database-Driven CRN with Location Privacy Assurance

We show that it is possible to achieve information theoretic location privacy for secondary users (SUs) in database-driven cognitive radio networks (CRNs) with an end-to-end delay less than a second, which is significantly better than that of the existing alternatives offering only a computational privacy. This is achieved based on a keen observation that, by the requirement of Federal Communications Commission (FCC), all certified spectrum databases synchronize their records. Hence, the same copy of spectrum database is available through multiple (distinct) providers. We harness the synergy between multi-server private information retrieval (PIR) and database- driven CRN architecture to offer an optimal level of privacy with high efficiency by exploiting this observation. We demonstrated, analytically and experimentally with deployments on actual cloud systems that, our adaptations of multi-server PIR outperform that of the (currently) fastest single-server PIR by a magnitude of times with information theoretic security, collusion resiliency, and fault-tolerance features. Our analysis indicates that multi-server PIR is an ideal cryptographic tool to provide location privacy in database-driven CRNs, in which the requirement of replicated databases is a natural part of the system architecture, and therefore SUs can enjoy all advantages of multi-server PIR without any additional architectural and deployment costs.Comment: 10 pages, double colum

Longitude : a privacy-preserving location sharing protocol for mobile applications

Location sharing services are becoming increasingly popular. Although many location sharing services allow users to set up privacy policies to control who can access their location, the use made by service providers remains a source of concern. Ideally, location sharing providers and middleware should not be able to access users’ location data without their consent. In this paper, we propose a new location sharing protocol called Longitude that eases privacy concerns by making it possible to share a user’s location data blindly and allowing the user to control who can access her location, when and to what degree of precision. The underlying cryptographic algorithms are designed for GPS-enabled mobile phones. We describe and evaluate our implementation for the Nexus One Android mobile phone

Continuous Variable Quantum State Sharing via Quantum Disentanglement

Quantum state sharing is a protocol where perfect reconstruction of quantum states is achieved with incomplete or partial information in a multi-partite quantum networks. Quantum state sharing allows for secure communication in a quantum network where partial information is lost or acquired by malicious parties. This protocol utilizes entanglement for the secret state distribution, and a class of "quantum disentangling" protocols for the state reconstruction. We demonstrate a quantum state sharing protocol in which a tripartite entangled state is used to encode and distribute a secret state to three players. Any two of these players can collaborate to reconstruct the secret state, whilst individual players obtain no information. We investigate a number of quantum disentangling processes and experimentally demonstrate quantum state reconstruction using two of these protocols. We experimentally measure a fidelity, averaged over all reconstruction permutations, of F = 0.73. A result achievable only by using quantum resources.Comment: Published, Phys. Rev. A 71, 033814 (2005) (7 figures, 11 pages

Prochlo: Strong Privacy for Analytics in the Crowd

The large-scale monitoring of computer users' software activities has become commonplace, e.g., for application telemetry, error reporting, or demographic profiling. This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy. The ESA design, and its Prochlo implementation, are informed by our practical experiences with an existing, large deployment of privacy-preserving software monitoring. (cont.; see the paper