Biblio

The revelations of Snowden show that hardware and software of devices may corrupt users' machine to compromise the security in various ways. To address this concern, Mironov and Stephen-Davidowitz introduce the Cryptographic Reverse Firewall (CRF) concept that is able to resist the ex-filtration of secret information for some compromised machine (Eurocrypt 2015). There are some applications of CRF deployed in many cryptosystems, but less studied and deployed in Attribute-Based Encryption (ABE) field, which attracts a wide range of attention and is employed in real-world scenarios (i.e., data sharing in cloud). In this work, we focus how to give a CRF security protection for a multi-authority ABE scheme and hence propose a multi-authority key-policy ABE scheme with CRF (acronym, MA-KP-ABE-CRF), which supports attribute distribution and non-monotonic access structure. To achieve this, beginning with revisiting a MA-KP-ABE with non-trivial combining non-monotonic formula, we then give the randomness of ciphertexts and secret keys with reverse firewall and give formal security analysis. Finally, we give a simulation on our MA-KP-ABE-CRF system based on Charm library whose the experimental results demonstrate practical efficiency.

Cloud computing is a new promising technology paradigm that can provide clients from the whole network with scalable storage resources and on-demand high-quality services. However, security concerns are raised when sensitive data are outsourced. Searchable encryption is a kind of cryptographic primitive that enables clients to selectively retrieve encrypted data, the existing schemes that support for sub-linear boolean queries are only considered in symmetric key setting, which makes a limitation for being widely deployed in many cloud applications. In order to address this issue, we propose a novel searchable asymmetric encryption scheme to support for sub-linear boolean query over encrypted data in a multi-client model that is extracted from an important observation that the outsourced database in cloud is continuously contributed and searched by multiple clients. For the purpose of introducing the scheme, we combine both the ideas of symmetric searchable encryption and public key searchable encryption and then design a novel secure inverted index. Furthermore, a detailed security analysis for our scheme is given under the simulation-based security definition. Finally, we conduct experiments for our construction on a real dataset (Enron) along with a performance analysis to show its practicality.

In cloud computing, computationally weak users are always willing to outsource costly computations to a cloud, and at the same time they need to check the correctness of the result provided by the cloud. Such activities motivate the occurrence of verifiable computation (VC). Recently, Parno, Raykova and Vaikuntanathan showed any VC protocol can be constructed from an attribute-based encryption (ABE) scheme for a same class of functions. In this paper, we propose two practical and efficient semi-adaptively secure key-policy attribute-based encryption (KP-ABE) schemes with constant-size ciphertexts. The semi-adaptive security requires that the adversary designates the challenge attribute set after it receives public parameters but before it issues any secret key query, which is stronger than selective security guarantee. Our first construction deals with small universe while the second one supports large universe. Both constructions employ the technique underlying the prime-order instantiation of nested dual system groups, which are based on the \$d\$-linear assumption including SXDH and DLIN assumptions. In order to evaluate the performance, we implement our ABE schemes using \$\textbackslashtextsf\Python\\$ language in Charm. Compared with previous KP-ABE schemes with constant-size ciphertexts, our constructions achieve shorter ciphertext and secret key sizes, and require low computation costs, especially under the SXDH assumption.

In cloud computing, computationally weak users are always willing to outsource costly computations to a cloud, and at the same time they need to check the correctness of the result provided by the cloud. Such activities motivate the occurrence of verifiable computation (VC). Recently, Parno, Raykova and Vaikuntanathan showed any VC protocol can be constructed from an attribute-based encryption (ABE) scheme for a same class of functions. In this paper, we propose two practical and efficient semi-adaptively secure key-policy attribute-based encryption (KP-ABE) schemes with constant-size ciphertexts. The semi-adaptive security requires that the adversary designates the challenge attribute set after it receives public parameters but before it issues any secret key query, which is stronger than selective security guarantee. Our first construction deals with small universe while the second one supports large universe. Both constructions employ the technique underlying the prime-order instantiation of nested dual system groups, which are based on the \$d\$-linear assumption including SXDH and DLIN assumptions. In order to evaluate the performance, we implement our ABE schemes using \$\textbackslashtextsf\Python\\$ language in Charm. Compared with previous KP-ABE schemes with constant-size ciphertexts, our constructions achieve shorter ciphertext and secret key sizes, and require low computation costs, especially under the SXDH assumption.

The recent growth of anonymous social network services – such as 4chan, Whisper, and Yik Yak – has brought online anonymity into the spotlight. For these services to function properly, the integrity of user anonymity must be preserved. If an attacker can determine the physical location from where an anonymous message was sent, then the attacker can potentially use side information (for example, knowledge of who lives at the location) to de-anonymize the sender of the message. In this paper, we investigate whether the popular anonymous social media application Yik Yak is susceptible to localization attacks, thereby putting user anonymity at risk. The problem is challenging because Yik Yak application does not provide information about distances between user and message origins or any other message location information. We provide a comprehensive data collection and supervised machine learning methodology that does not require any reverse engineering of the Yik Yak protocol, is fully automated, and can be remotely run from anywhere. We show that we can accurately predict the locations of messages up to a small average error of 106 meters. We also devise an experiment where each message emanates from one of nine dorm colleges on the University of California Santa Cruz campus. We are able to determine the correct dorm college that generated each message 100\textbackslash% of the time.