Nowadays, with the rise of location-based services, the personalized sequential POI recommendation has become a pivotal element for enhancing customer experiences. Although many previous POI recommendation models have shown promising results and improvements in this area, several challenges still exist in this field. Firstly, the previous sequential recommenders do not well-utilize the geographical features that are highly affecting the user’s future choices of visits. Furthermore, the self-attention mechanism, which is a popular method used in sequential POI recommendation, has a limitation in treating the input user sequence as an unordered set. Using positional embedding is a typical way to overcome this limitation. However, the use of such embeddings may potentially restrict the model’s ability to learn meaningful patterns in user preferences among POIs. To address these challenges, we propose GeoMixer, a novel MLP-based sequential POI recommender that incorporates travel routing distance to capture geographical features and leverages Multi-layer Perceptron (MLP) architecture to model the spatial and sequential patterns in the sequential POI recommendations. By adopting MLP mixing layers, GeoMixer has the capability of memorizing the chronological order of the input POIs without the positional embedding and can emphasize the important latent features of each POI. The use of the travel routing information improves the model’s ability of capturing spatial patterns during the model learning process. Extensive experiments on real-world datasets show that GeoMixer outperforms state-of-theart methods in various metrics, highlighting the significance of incorporating travel routing distance and leveraging MLP architecture in sequential POI recommendation systems.

Privacy-preserving traffic flow classification has attracted a significant amount of research interest because of its increasing importance to both network management and privacy protection. In this paper, we propose novel methods for effectively protecting network flow identifiers and attributes against privacy inference attacks in port-based and payload-based classifications respectively, and analyze their performance guarantee on data utility for flow classification and privacy (security). For protection of flow identifiers, we propose a partial identifier protection approach applying randomization and anonymization respectively on desired bit positions to conceal sensitive information, and show their expected-case performance guarantee. For protection of flow attributes, we propose a perturbation-based scheme that first selects the representative attributes by deploying an entropy-based attribute selection method to filter out redundant and insignificant attributes and reduce the problem space, then partitions the attribute domains into either equal-depth or equal-width intervals and perturbs attribute values in these intervals by swapping them with those in adjacent intervals and intervals with same value distribution respectively. We analyze the performance guarantee of the proposed methods and show the experiment results of classification accuracy obtained by implementing popular machine-learning based benchmark classifiers on our selected attributes against that on raw attributes, and on our perturbed attribute values against that on raw values, respectively. The experiment results show that our proposed methods for attribute selection and perturbation retain a high degree of data utility under the desired privacy guarantee for network traffic classification.

We address the problems of privacy-preserving duplicate tuple matching (PPDTM) and privacy-preserving threshold attributes matching (PPTAM) in the scenario of a horizontally partitioned database among N parties, where each party holds a private share of the database's tuples and all tuples have the same set of attributes. In PPDTM, each party determines whether its tuples have any duplicate on other parties' private databases. In PPTAM, each party determines whether all attribute values of each tuple appear at least a threshold number of times in the attribute unions. We propose protocols for the two problems using additive homomorphic cryptosystem based on the subgroup membership assumption, e.g., Paillier's and ElGamal's schemes. By analysis on the total numbers of modular exponentiations, modular multiplications and communication bits, with a reduced computation cost which dominates the total cost, by trading off communication cost, our PPDTM protocol for the semihonest model is superior to the solution derivable from existing techniques in total cost. Our PPTAM protocol is superior in both computation and communication costs. The efficiency improvements are achieved mainly by using random numbers instead of random polynomials as existing techniques for perturbation, without causing successful attacks by polynomial interpolations. We also give detailed constructions on the required zero-knowledge proofs and extend our two protocols to the malicious model, which were previously unknown.