An online ride-hailing (ORH) service, such as Uber and Didi Chuxing, can provide on-demand transportation service to users via mobile phones, which brings great convenience to people's daily lives. ...Along with the convenience, high privacy concerns are also raised when using an ORH service since users and drivers must share their real-time locations with the ORH server, which results in the leakage of the mobility patterns and additional privacy of users and drivers. In this paper, we propose a privacy-preserving ride-matching scheme, called pRide, for ORH service. pRide allows an ORH server to efficiently match rider and drivers based on their distances in the road network without revealing the location privacy of riders and drivers. Specifically, we make use of the road network embedding technique together with cryptographic primitives and design a scheme to securely and efficiently estimate the shortest distances between riders and drivers in road networks approximately. Moreover, by incorporating garbled circuits, the proposed scheme is able to output the nearest driver around a rider. We implement the scheme and evaluate it on the representative real-world datasets. The theoretical analysis and experimental results demonstrate that pRide achieves an efficient, secure, and yet accurate ride matching for ORH service.
Fuzzy keyword search is a necessary and important feature of information retrieval in modern cloud storage services since users with insufficient knowledge may input typos or keywords with ...inconsistent formats. However, most of the existing fuzzy search schemes adopt bloom filter and locality sensitive hashing which cannot resist Sparse Non-negative Matrix Factorization based attack (SNMF attack). To this end, we propose a new secure multi-keyword fuzzy search scheme for encrypted cloud data, our scheme leverages random redundancy method to handle the deterministic of bloom filter to resist SNMF attack. Besides the privacy, our scheme uses tree-based index construction to improve search efficiency and allows users to conduct complicated fuzzy search with logic operations “AND”, “OR” and “NOT”, which can meet more flexible and fine-grained query demands. The theoretical analysis and experiments on real-world data show the security and high performance of our scheme.
In this paper, the properties of symbol transformations of cyclic Hadamard matrices are studied. An infinite class of (n−1)-variable 2-resilient rotation symmetric Boolean functions are constructed, ...and the nonlinearity of the constructed functions is 2n(n−1). The crucial technique of this method is to determine a subset T⊆F2n−1 satisfying a correspondent condition. This is a new construction of 2-resilient rotation symmetric Boolean functions via switching the supports of (n−1)-variable rotation symmetric Boolean functions of degree one, i.e., f0n−1(x1,x2,⋯,xn−1)=⊕i=1n−1xi, where n=4t.
Solving large-scale systems of linear equations (L-SLE) is a common scientific and engineering computational task. But such problem involves enormous computing resources, which is burdensome for the ...resource-limited clients. Cloud computing enables computational resource-limited clients to economically outsource such problems to the cloud server. However, outsourcing LSLE to the cloud brings great security concerns and challenges since the LSLE usually contains sensitive information. Previous works for secure outsourcing LSLE are mainly based on iterative methods which cause heavy computation cost for the client side. And they usually neglect to protect the number and position privacy of zero elements in the coefficient matrix, which is not secure enough for many applications. In this paper, with a series of disguise-based techniques, we propose a new efficient and non-iterative algorithm for securely outsourcing LSLE. Our algorithm only requires two rounds of communication between the client and cloud. Furthermore, the number and positions of zero elements in coefficient matrix can be hidden from the cloud with low computational complexity. Finally, we provide extensive theoretical analysis and experimental evaluation to show its high-efficiency and security compared to the previous works.
Cloud computing has been envisioned as the next generation architecture of the IT enterprise, but there exist many security problems. A significant problem encountered in the context of cloud storage ...is whether there exists some potential vulnerabilities towards cloud storage system after introducing third parties. Public verification enables a third party auditor (TPA), on behalf of users who lack the resources and expertise, to verify the integrity of the stored data. Many existing auditing schemes always assume TPA is reliable and independent. This work studies the problem what if certain TPAs are semi-trusted or even potentially malicious in some situations. Actually, the authors consider the task of allowing such a TPA to involve in the audit scheme. They propose a feedback-based audit scheme via which users are relaxed from interacting with cloud service provider (CSP) and can check the integrity of stored data by themselves instead of TPA yet. Specifically, TPA generates the feedback through processing the proof from CSP and returns it to user which is yet unforgeable to TPA and checked exclusively by user. Through detailed security and performance analysis, the author's scheme is shown to be more secure and lightweight.
For differential cryptanalysis under the single-key model, the key schedules hardly need to be exploited in constructing the characteristics, which is based on the hypothesis of stochastic ...equivalence. In this paper, we study a profound effect of the key schedules on the validity of the differential characteristics. Noticing the sensitivity in the probability of the characteristics to specific keys, we label the keys where a characteristic has nonzero probability by
effective keys
. We propose the concept of
singular characteristics
which are characteristics with no effective keys, and exploit an algorithm to sieve them out by studying the key schedule. We show by a differential characteristic of PRINCE whose expected differential probability is much larger than that of a random permutation, i.e.,
2
-
35
vs.
2
-
64
. Yet, it is indeed singular which could be mis-used to mount a differential attack. Singular characteristics are found for 3-round AES and 3-round Midori-128 as well. Furthermore, taking the possible mismatches of the effective keys in a number of differential characteristics into consideration, we present
singular clusters
which indicates an empty intersection of the corresponding effective keys, and this is evidenced by showing two differential characteristics of the 2-round AES. We also show that characteristics are tightly linked to the key schedule, as shown in the paper, a valid characteristic in the AES-128 can be singular for the AES-192. Our results indicate a gap over the perspectives of the designers and the attackers, which warns the latter to validate the theoretically-built distinguishers. Therefore, a closer look into the characteristics is inevitable before any attack is claimed.
Collaborative filtering is a popular approach for building an efficient and scalable recommender system. However, it has not unleashed its full potential due to the following problems. (1) Serious ...privacy concerns: collaborative filtering relies on aggregated user data to make personalized predictions, which means that the centralized server can access and compromise user privacy. (2) Expensive resources required: conventional collaborative filtering techniques require a server with powerful computing capacity and large storage space, so that the server can train and maintain the model. (3) Considering only one form of user feedback: most existing works aim to model user preferences based on explicit feedback (e.g., ratings) or implicit feedback (e.g., purchase history, viewing history) due to their heterogeneous representation; however, these two forms of feedback are abundant in most collaborative filtering applications, can both affect the model, and very few works studied the simultaneous use thereof. To solve the above problems, in this study we focus on implementing decentralized probabilistic matrix factorization for privacy-preserving recommendations. First, we explore the existing collaborative filtering algorithms and propose a probabilistic matrix co-factorization model. By integrating explicit and implicit feedback into a shared probabilistic model, the model can cope with the heterogeneity between these two forms of feedback. Further, we devise a decentralized learning method that allows users to keep their private data on the end devices. A novel decomposing strategy is proposed for users to exchange only non-private information, in which stochastic gradient descent is used for updating the models. Complexity analysis proves that our method is highly efficient with linear computation and communication complexity. Experiments conducted on two real-world datasets FilmTrust and Epinions show that our model gains a guarantee of convergence as the RMSE decreases quickly within 100 rounds of iterations. Compared with the state-of-the-art models, our model achieves lower model loss in rating prediction task and higher precision in item recommendation task.
With the proliferation of cloud platforms, users are inclined to outsource their encrypted data to cloud platforms. However, the existing encryption schemes only provide users with limited query ...types over encrypted data. Meanwhile, if users encrypt their data by deterministic encryption, their encrypted data will be easily subject to the frequency attack. Besides, users' data privacy is likely to be disclosed to the cloud server when their encrypted data is updated on cloud platforms. To address these problems, in this paper, we propose an effective encrypted query scheme over outsourcing data on cloud platforms. In our scheme, users' data is encrypted based on all possible queries to meet users' diverse query demands. Furthermore, a double AES encryption method is adopted to cope with the frequency attack existing in deterministic encryption. To protect users' privacy when their data is updated, a neighbor rows exchange method is designed in our scheme. The theoretical analysis and comparative experiments demonstrate the effectiveness of our scheme.
Mobile sensing mines group information through sensing and aggregating users’ data. Among major mobile sensing applications, the distinct counting problem aiming to find the number of distinct ...elements in a data stream with repeated elements, is extremely important for avoiding waste of resources. Besides, the privacy protection of users is also a critical issue for aggregation security. However, it is a challenge to meet these two requirements simultaneously since normal privacy-preserving methods would have negative influence on the accuracy and efficiency of distinct counting. In this paper, we propose a Privacy-Preserving Distinct Counting scheme (PPDC) for mobile sensing. Through integrating the basic idea of homomorphic encryption into Flajolet-Martin (FM) sketch, PPDC allows an aggregator to conduct distinct counting over large-scale datasets without disrupting privacy of users. Moreover, PPDC supports various forms of sensing data, including camera images, location data, etc. PPDC expands each bit of the hashing values of users’ original data, FM sketch is thus enhanced for encryption to protect users’ privacy. We prove the security of PPDC under known-plaintext model. The theoretic and experimental results show that PPDC achieves high counting accuracy and practical efficiency with scalability over large-scale data sets.
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