ABAKA : a novel attribute-based k-anonymous collaborative solution for LBSs

The increasing use of mobile devices, along with advances in telecommunication systems, increased the popularity of Location-Based Services (LBSs). In LBSs, users share their exact location with a potentially untrusted Location-Based Service Provider (LBSP). In such a scenario, user privacy becomes a major con- cern: the knowledge about user location may lead to her identification as well as a continuous tracing of her position. Researchers proposed several approaches to preserve users’ location privacy. They also showed that hiding the location of an LBS user is not enough to guarantee her privacy, i.e., user’s pro- file attributes or background knowledge of an attacker may reveal the user’s identity. In this paper we propose ABAKA, a novel collaborative approach that provides identity privacy for LBS users considering users’ profile attributes. In particular, our solution guarantees p -sensitive k -anonymity for the user that sends an LBS request to the LBSP. ABAKA computes a cloaked area by collaborative multi-hop forwarding of the LBS query, and using Ciphertext-Policy Attribute-Based Encryption (CP-ABE). We ran a thorough set of experiments to evaluate our solution: the results confirm the feasibility and efficiency of our proposal

Lower Bounds for Oblivious Near-Neighbor Search

We prove an $\Omega(d \lg n/ (\lg\lg n)^2)$ lower bound on the dynamic cell-probe complexity of statistically $\mathit{oblivious}$ approximate-near-neighbor search ($\mathsf{ANN}$) over the $d$-dimensional Hamming cube. For the natural setting of $d = \Theta(\log n)$, our result implies an $\tilde{\Omega}(\lg^2 n)$ lower bound, which is a quadratic improvement over the highest (non-oblivious) cell-probe lower bound for $\mathsf{ANN}$. This is the first super-logarithmic $\mathit{unconditional}$ lower bound for $\mathsf{ANN}$ against general (non black-box) data structures. We also show that any oblivious $\mathit{static}$ data structure for decomposable search problems (like $\mathsf{ANN}$) can be obliviously dynamized with $O(\log n)$ overhead in update and query time, strengthening a classic result of Bentley and Saxe (Algorithmica, 1980).Comment: 28 page

Detecting Outlier Patterns with Query-based Artificially Generated Searching Conditions

In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, national security, etc. However, sub-graph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this work, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined on a real-world academic network, using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs, and is robust to the choice of similarity measures