Recently, edge-assisted methods have been proposed as a promising technique to deliver fast and accurate on-device video analytics by partitioning frame data and dispatching them to edge servers for parallel execution. However, the data partition (DP) reduces the detection latency but decreases accuracy since objects may cross the boundaries of adjacent blocks. The effect of DP on the accuracy and latency depends on multiple vital parameters (e.g., target size, density, network, and computing resources) in an unknown and time-varying fashion. Moreover, these parameters are determined by the application scenarios and edge environment, which are uncertain and heterogeneous at the edge. Hence, how to partition frames to strike a balance between accuracy and latency is a nontrivial and intractable problem. To this end, we propose an online learning-based device-edge–cloud collaboration framework, ADPTD, to guide DP at the edge. We propose an optimal task dispatching algorithm (OTD) to minimize detection latency. Then, we propose a multiarmed bandit-based algorithm to pick a DP strategy and invoke OTD to dispatch tasks in each time slot. Theoretical analysis reveals that ADPTD achieves sublinear regret. Extensive experimental results show that ADPTD outperforms the state-of-the-art methods, achieving a latency reduction of up to$2.53\times $and improving accuracy by up to 49.4%.

Location-based services (LBSs) in vehicular ad hoc networks (VANETs) offer users numerous conveniences. However, the extensive use of LBSs raises concerns about the privacy of users' trajectories, as adversaries can exploit temporal correlations between different locations to extract personal information. Additionally, users have varying privacy requirements depending on the time and location. To address these issues, this paper proposes a personalized trajectory privacy protection mechanism (PTPPM). This mechanism first uses the temporal correlation between trajectory locations to determine the possible location set for each time instant. We identify a protection location set (PLS) for each location by employing the Hilbert curve-based minimum distance search algorithm. This approach incor-porates the complementary features of geo-indistinguishability and distortion privacy. We put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. This mechanism generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that our mechanism outperforms the benchmark by providing enhanced privacy protection while meeting user's QoS requirements.

Location privacy protection (LPP) has become a key concern during mobile crowdsourcing (MCS) task allocation. Existing LPP mechanisms for MCS applications mainly focus on two-dimensional (2D) plane scenarios or directly apply 2D techniques into three-dimensional (3D) space scenarios, leaving the height dimension of 3D geolocation vulnerable to privacy breaches. To facilitate the LPP in 3D MCS, we propose a learning-based geo-perturbation mechanism using 3D geo-indistinguishability (3D-GI). In this mechanism, we first define an optimization objective to balance location privacy and MCS server profit, making it adaptable to different types of MCS applications. Then, we adopt the Asynchronous Advantage Actor-Critic (A3C) algorithm to design a reinforcement learning (RL)-based approach without knowing the accurate system and attack models. This approach enables us to derive the optimal perturbation policy in continuous policy space and accelerates the learning speed using asynchronous multi-thread training. Simulation results demonstrate that the proposed mechanism can better balance location privacy and server profit in 3D MCS applications compared to existing benchmarks.

The rapid development of indoor location-based services (LBS) has raised concerns about location privacy protection in the 3-dimensional (3D) space. The existing 2-dimensional (2D) location privacy protection mechanisms (LPPMs) cannot effectively resist attacks in 3D environments. Furthermore, users may have various sensitive attributes at different locations and times. In this paper, we first formally study the relationship between two complementary notions of geo-indistinguishability and distortion privacy (i.e., expected inference error) in the 3D space and develop a two-phase personalized 3D LPPM (P3DLPPM). In Phase I, we search for neighboring locations to formulate a protection location set (PLS) for hiding the actual location based on the above-mentioned relationship. To realize this, we develop a 3D Hilbert curve-based minimum distance searching algorithm to find the PLS with minimum diameter for each location while guaranteeing differential privacy. In Phase II, we put forth a novel Permute-and-Flip mechanism for location perturbation, which maps its initial application in data publishing privacy protection to a location perturbation mechanism. It generates fake locations with smaller perturbation distances while improving the balance between privacy and quality of service (QoS). Simulation results show that the proposed P3DLPPM can significantly improve personalized privacy protection while meeting the user's QoS needs.

For providing accurate and fast on-device high-resolution video analytics, edge-assisted methods are widely proposed by using lower-resolution frames and splitting them with overlap padding. The use of low-resolution frames can significantly reduce the computational workload of video analytics. Dividing a frame into multiple overlapping parts simultaneously improves parallelism and maintains processing accuracy. However, accuracy and latency serve as a pair of tradeoff metrics, and prioritizing one to optimize overlap padding or resolution selection will compromise the other metric. Fortunately, we have discovered that in real-world video analytics scenarios with varying object sizes, it is not necessary to simultaneously achieve a high overlap padding size and high resolution. Hence, how to set appropriate overlap padding size and resolution to strike a balance between accuracy and latency in practical scenarios is a nontrivial and intractable problem. To this end, we propose an online learning-based method to achieve adaptive overlap padding and resolution selection, called APR. We model the problem as an integer programming and propose a Muli-armed bandit (MAB) theory-based algorithm to solve it. We discretize the continuum overlap padding size into a finite set to narrow explore space and set the frame split strategy as context information to achieve fast convergence. Theoretical analysis reveals APR achieves sub-linear regret. Extensive experimental results show APR outperforms the benchmark methods, achieving up to 2.06 × speedup in terms of latency and 0.18× increase in accuracy.