Communications of the ACM (CACM for short, not the best sounding acronym around) is the ACM’s flagship magazine. Started in 1957, CACM is handy for keeping up to date on current research being carried out across all topics of computer science and realworld applications. CACM has had an illustrious past with many influential pieces of work and debates started within its pages. These include Hoare’s presentation of the Quicksort algorithm; Rivest, Shamir and Adleman’s description of the first publickey cryptosystem RSA; and Dijkstra’s famous letter against the use of GOTO. In addition to the print edition, which is released monthly, there is a fantastic website (http://cacm.acm. org/) that showcases not only the most recent edition but all previous CACM articles as well, readable online as well as downloadable as a PDF. In addition, the website lets you browse for articles by subject, a handy feature if you want to focus on a particular topic. CACM is really essential reading. Pretty much guaranteed to contain content that is interesting to anyone, it keeps tabs on the latest in computer science. It is a valuable asset for us students, who tend to delve deep into a particular area of CS and forget everything that is happening around us. — Daniel Gooch U ndergraduate research is like a box of chocolates: You never know what kind of project you will get. That being said, there are still a few things you should know to get the most out of the experience.

Communications of the ACM

Privacy has become a considerable issue when the applications of big data are dramatically growing in cloud computing. The benefits of the implementation for these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The execution time of the data encryption is one of the serious issues during the data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level companioning with privacy concerns. In this paper, we concentrate on privacy and propose a novel data encryption approach, which is called Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data and use privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements. The performance of D2ES has been evaluated in our experiments, which provides the proof of the privacy enhancement.

Privacy-Preserving Data Encryption Strategy for Big Data in Mobile Cloud Computing

Enhancing privacy through uniform grid and caching in location-based services☆

With the rapid development of cloud computing, an increasing number of individuals and organizations are sharing data in the public cloud. To protect the privacy of data stored in the cloud, a data owner usually encrypts his data in such a way that certain designated data users can decrypt the data. This raises a serious problem when the encrypted data needs to be shared to more people beyond those initially designated by the data owner. To address this problem, we introduce and formalize an identity-based encryption transformation (IBET) model by seamlessly integrating two well-established encryption mechanisms, namely identity-based encryption (IBE) and identity-based broadcast encryption (IBBE). In IBET, data users are identified and authorized for data access based on their recognizable identities, which avoids complicated certificate management in usual secure distributed systems. More importantly, IBET provides a transformation mechanism that converts an IBE ciphertext into an IBBE ciphertext so that a new group of users not specified during the IBE encryption can access the underlying data. We design a concrete IBET scheme based on bilinear groups and prove its security against powerful attacks. Thorough theoretical and experimental analyses demonstrate the high efficiency and practicability of the proposed scheme.

Identity-Based Encryption Transformation for Flexible Sharing of Encrypted Data in Public Cloud

Since homomorphic encryption operations have high computational complexity, image applications based on homomorphic encryption are often time consuming, which makes them impractical. In this paper, we study efficient encrypted image applications with the encrypted domain Walsh-Hadamard transform (WHT) and parallel algorithms. We first present methods to implement real and complex WHTs in the encrypted domain. We then propose a parallel algorithm to improve the computational efficiency of the encrypted domain WHT. To compare the WHT with the discrete cosine transform (DCT), integer DCT, and Haar transform in the encrypted domain, we conduct theoretical analysis and experimental verification, which reveal that the encrypted domain WHT has the advantages of lower computational complexity and a shorter running time. Our analysis shows that the encrypted WHT can accommodate plaintext data of larger values and has better energy compaction ability on dithered images. We propose two encrypted image applications using the encrypted domain WHT. To accelerate the practical execution, we present two parallelization strategies for the proposed applications. The experimental results show that the speedup of the homomorphic encrypted image application exceeds 12.

Efficient Encrypted Images Filtering and Transform Coding With Walsh-Hadamard Transform and Parallelization

For ranked search in encrypted cloud data, order preserving encryption (OPE) is an efficient tool to encrypt relevance scores of the inverted index. When using deterministic OPE, the ciphertexts will reveal the distribution of relevance scores. Therefore, Wang et al. proposed a probabilistic OPE, called one-to-many OPE, for applications of searchable encryption, which can flatten the distribution of the plaintexts. In this paper, we proposed a differential attack on one-to-many OPE by exploiting the differences of the ordered ciphertexts. The experimental results show that the cloud server can get a good estimate of the distribution of relevance scores by a differential attack. Furthermore, when having some background information on the outsourced documents, the cloud server can accurately infer the encrypted keywords using the estimated distributions.

Security Analysis on One-to-Many Order Preserving Encryption-Based Cloud Data Search

Signal processing in the encrypted domain becomes a desired technique to protect privacy of outsourced data in cloud. In this article, we propose a double-cipher scheme to implement nonlocal means (NLM) denoising in encrypted images. In this scheme, one ciphertext is generated by the Paillier scheme, which enables the mean filter, and the other is obtained by a privacy-preserving transform, which enables the nonlocal search. By the privacy-preserving transform, the cloud server can search the similar pixel blocks in the ciphertexts with the same speed as in the plaintexts; thus, the proposed method can be executed fast. To enhance the security, we randomly permutate both ciphertexts. To reduce the denoising complexity caused by random permutation, a random NLM method is exploited in the encrypted domain. The experimental results show that the quality of denoised images in the encrypted domain is comparable to that obtained in the plain domain.

Secure Nonlocal Denoising in Outsourced Images

Cloud computing is becoming more and more popular recently, and this trend encourages large corporations and companies to outsource their data to a remote cloud server. However, the concern of data privacy and security is a huge problem which impedes the development of cloud computing. It is essential and necessary to propose a scheme to realize the information retrieval and protect the data privacy at the same time. Since ranked multi-keyword search is a pretty practical question in this area and it has a significant influence on the user experience, we would mainly focus on the scheme to achieve high performance and low cost multi-keyword ranked search. In this paper, we proposed a novel search scheme with Johnson-Lindenstrauss transform (J-L scheme for short) and a technique called "optimised maximum query" to live up to our efficiency demand. Furthermore, we established a complete system to guarantee a set of strict privacy requirements. Extensive experiments on real-world database are presented to show our proposed scheme's efficiency and accuracy.

http://or.nsfc.gov.cn/bitstream/00001903-5/419712/1/1000007116161.pdf

An Efficient Multi-keyword Ranked Retrieval Scheme with Johnson-Lindenstrauss Transform over Encrypted Cloud Data

In this paper, we focus on the fuzzy keyword search problem over the encrypted cloud data in the cloud computing and propose a novel Two-Step-Bloom-Secure-Filter (TSBSF) scheme based on Bloom filter to realize the efficiency and flexibility of data use. The proposed scheme not only reduces the space complexity significantly but also supports the data update with low time complexity and guarantees the search accuracy. Experimental results on real world data have certified the validity and practicality of this novel method.

A novel fuzzy keyword retrieval scheme over encrypted cloud data

The present invention provides a privacy protection cloud image management system which comprises an encryption unit, an image hash encryption unit and a decryption unit, wherein the encryption unit is used for carrying out encryption on a plaintext image added by a user and uploading an obtained ciphertext image to a cloud server; the image hash encryption unit is used for calculating a hash value of the plaintext image added by the user through adopting a perceptual hash algorithm, transforming the hash value into a ciphertext hash value by adopting a transformation algorithm and then uploading the plaintext hash value and the plaintext image to the cloud server together; the image hash encryption unit is also used for calculating a hash value of an inquiry reference image input by the user through adopting the perceptual hash algorithm, then transforming the hash value into a ciphertext hash value by adopting the transformation algorithm and uploading the ciphertext hash value to the cloud server; and the decryption unit is used for carrying out decryption on an inquiry result returned by the cloud server and feeding back the obtained plaintext image to the user According to the present invention, both management and retrieval of the cloud server for the image input by the user are directly carried out on a ciphertext, so that user privacy is effectively prevented from being leaked and safety of user data is improved

Ke Li

Papers

Security Analysis on One-to-Many Order Preserving Encryption-Based Cloud Data Search

Secure Nonlocal Denoising in Outsourced Images

An Efficient Multi-keyword Ranked Retrieval Scheme with Johnson-Lindenstrauss Transform over Encrypted Cloud Data

A novel fuzzy keyword retrieval scheme over encrypted cloud data

Privacy protection cloud image management system