Running with music, it can not only capture attention, trigger emotions, adjust or control mood, encourage rhythmic movement, but also achieve higher values of maximal heart rate with music than without music. Moreover, when a runner run with the difference music tempo such as slowtempo, medium-tempo, and fast-tempo, the runner will achieve differences maximal heart rate and running performance. In general, the training benefit will be separate to 5 target zones by the percent of maximum heart rate range. However, running control without notification and trigger is hard to achieve the benefit in the specific target zones. In this paper, a conceptual framework that facilitates the development for enhancing the running motivation by automatic matching difference music tempo and specific training load in real-time is proposed. Specifically, it provides an essential mechanism that enables the running motivation service to achieve the training benefit for developers and then builds into the corresponding services.

Both data utility and data privacy must be carefully considered when datasets containing users’ sensitive information are released for use beyond the scope of the data‑collecting organization. To achieve an appropriate balance between data utility and data privacy in such released datasets, a variety of privacy preservation models have been proposed, including k‑Anonymity, l‑Diversity, Anatomy, t‑Closeness, and Differential privacy. Unfortunately, these privacy preservation models can sufficiently address concerns of privacy violation issues in simple datasets. Moreover, to the best of our knowledge about them, they still have various vulnerabilities that must be improved such as data utility, complexity, and the new privacy violation techniques that are discovered after they are proposed. Furthermore, we found that they are not well-suited to address concern of privacy violation issues in datasets containing content-sensitive values (or sometimes they are called content-based datasets). To rid these vulnerabilities, a new privacy preservation model is proposed in this work, it is called (d, c, l)-Privacy. It is proposed to address concerns of privacy violation issues in content-sensitive datasets. It is based on expert and mechanism term document measurements. That is, the released datasets can reduce about the concern of privacy violation issues after they are satisfied by the d, c, and l parameters. To achieve (d, c, l)-Privacy constraints, there are three algorithms that are proposed in this work, i.e., SCFS, greedy, and optimal (d, c, l)-Privacy algorithms. The aim of SCFS (d, c, l)-Privacy is that aside from the released dataset is satisfied by the d, c, and l parameters, the execution time is maintained as much as possible. While the greedy (d, c, l)-Privacy will be mindful of both the execution time and data utility. Another proposed algorithm, the optimal (d, c, l)-Privacy algorithm, aims to maintain the meaning or usefulness of the data as much as possible.The experimental results show that the proposed algorithms are effective in mitigating privacy breaches in released datasets under the (d, c, l)-Privacy constraints. Among the evaluated algorithms, FCFS achieves the highest time efficiency, whereas the greedy algorithm offers a better balance by preserving data semantics within a reasonable computational cost. The optimal algorithm consistently maintains the highest level of data utility, albeit at increased computational expense. These findings indicate that the proposed algorithms are not only effective in preserving privacy data in released datasets but are also suitable for practical deployment in real‑world data‑releasing scenarios.

Location-based services (LBS) are well-known services that provide a user’s position and deliver tailored experiences. They are generally used for getting from one location to another, tracking, mapping, and timing, and they are often available in smartphones, tablets, computers, and applications such as Facebook, Twitter, TikTok, and YouTube. Aside from these, the data is collected by location-based services, which can be provided to the data analyst for some business reasons, such as improving marketing strategies, organizational policies, and customer services. In this situation, it can lead to privacy violation concerns. To reduce these concerns when location-based data is provided to the data analyst or released to be utilized outside the scope of data collecting organizations, several privacy preservation models have been proposed, such as k-Anonymity, l-Diversity, t-Closeness, LKC-Privacy, differential privacy, and location-based privacy preservation models. Unfortunately, to the best of our knowledge about these privacy preservation models, they still have several vulnerabilities regarding privacy violation concerns that must be addressed when location-based data is released, i.e., privacy violation issues from inferring sensitive locations (e.g., specialized hospitals, pawnshops, prisons, and safe house), privacy violation issues from considering duplicate trajectory paths (i.e., although the user’s visited path duplicate with other paths, it still has privacy violation issues when it consists of a sensitive location), and privacy violation issues from considering unique locations (e.g., home, condominium, and office). Moreover, these privacy preservation models have data utility issues and data transformation complexity that must be improved. To address these vulnerabilities, a new privacy preservation model, (ξ, ϵ)-Privacy, is proposed in this work. It is based on data generalization and data suppression in conjunction with data sliding windows and R-Tree, such that there are no concerns about privacy violations in its released location-based data from using privacy violation issues from inferring sensitive locations, privacy violation issues from considering duplicate trajectory paths, and privacy violation issues from considering unique locations. It is highly efficient and effective in data maintenance. Furthermore, we show that the proposed model is efficient and effective through extensive experiments.

Environmental conditions in Laboratory Animal Resource Centers (LARCs), including ammonia, air quality, temperature, humidity, light intensity, and sound level, directly affect animal welfare and experimental reproducibility. Most LARC facilities rely on manual inspection or single-parameter systems that cannot detect threshold exceedances before physiological effects occur. This paper presents a Cyber–Physical System for multi-parameter LARC environmental monitoring that addresses three gaps: an ESP32-based sensor node integrating five sensor types in a device-agnostic, web-configurable design; a per-device, per-sensor configurable policy engine enabling institution-specific threshold compliance without code changes; and a configurable escalating-interval alert dispatcher that consolidates exceedances, lengthens the repeat interval under sustained exceedance, and resets upon new violations, reducing alert fatigue while preserving prompt notification. Evaluation comprised functional testing of 64 cases (40 executed; 62.5% coverage; 87.5% executed pass rate; 54.7% coverage-weighted pass rate), latency analysis (worst case: 10 min), alert-repeat verification (five scenarios passed), API performance tests, and a 32-user perception study suggesting perceived improvements. All five defects were resolved and retested. Monitoring and alerting functions operate as designed under the conditions evaluated; administrative functions were tested only in part. Firmware and calibration data are publicly available.

Location-based services (LBSs), which are used for navigation, tracking, and mapping across digital devices and social platforms, establish a user’s position and deliver tailored experiences. Collecting and sharing such trajectory datasets with analysts for business purposes raises critical privacy concerns, as both symmetry in recurring behavior mobility patterns and asymmetry in irregular movement mobility patterns in sensitive locations collectively expose highly identifiable information, resulting in re-identification risks, trajectory disclosure, and location inference. In response, several privacy preservation models have been proposed, including k-anonymity, l-diversity, t-closeness, LKC-privacy, differential privacy, and location-based approaches. However, these models still exhibit privacy issues, including sensitive location inference (e.g., hospitals, pawnshops, prisons, safe houses), disclosure from duplicate trajectories revealing sensitive places, and the re-identification of unique locations such as homes, condominiums, and offices. Efforts to address these issues often lead to utility loss and computational complexity. To overcome these limitations, we propose a new ($\xi$, $ϵ$)-privacy model that combines data generalization and suppression with sliding windows and R-Tree structures, where sliding windows partition large trajectory graphs into simplified subgraphs, R-Trees provide hierarchical indexing for spatial generalization, and suppression removes highly identifiable locations. The model addresses both symmetry and asymmetry in mobility patterns by balancing generalization and suppression to protect privacy while maintaining data utility. Symmetry-driven mechanisms that enhance resistance to inference attacks and support data confidentiality, integrity, and availability are core requirements of cryptography and information security. An experimental evaluation on the City80k and Metro100k datasets confirms that the ($\xi$, $ϵ$)-privacy model addresses privacy issues with reduced utility loss and efficient scalability, while validating robustness through relative error across query types in diverse analytical scenarios. The findings provide evidence of the model’s practicality for large-scale location data, confirming its relevance to secure computation, data protection, and information security applications.

The release of datasets containing sensitive user information requires a careful balance between data utility and privacy preservation. To address this challenge, numerous privacy preservation models have been proposed, including k-Anonymity, l-Diversity, t-Closeness, and Differential privacy. However, these models are largely designed for simple datasets in which each attribute is represented by a single (atomic) value, limiting their effectiveness in more complex data environments. Specifically, k-Anonymity and its variants have been widely adopted to mitigate privacy risks arising from quasi-identifier-based inference attacks. While l-Diversity and t-Closeness are extended from k-Anonymity to address the disclosure of sensitive attributes. However, they are primarily effective when sensitive attributes are singular and well defined, which restricts their applicability in scenarios involving complex or content-based data. Another prominent approach is Differential privacy and its variants, which rely on probabilistic mechanisms and the introduction of random noise into query outputs. It provides strong theoretical guarantees and is well suited for numerical data and computation-driven applications. However, it is also less effective for content-based datasets, where semantic meaning and contextual integrity are essential and cannot be preserved through randomization. To overcome these limitations, this study proposes a new privacy preservation model, $(d,c,l)$-Privacy, specifically designed for content-based datasets. The proposed model ensures that released datasets satisfy the constraints defined by parameters d, c, and l, thereby mitigating potential privacy violations. To enforce these constraints, three algorithms are introduced, i.e., FCFS, greedy, and optimal $(d,c,l)$-privacy algorithms. The FCFS algorithm prioritizes computational efficiency while maintaining acceptable privacy guarantees. The greedy algorithm balances execution time and data utility. While the optimal algorithm focuses on maximizing semantic preservation and overall data usefulness, albeit at a higher computational cost. Experimental results show that the proposed algorithms effectively mitigate privacy risks in released datasets under $(d,c,l)$-privacy constraints. Among the evaluated algorithms, FCFS achieves the highest computational efficiency, while the greedy algorithm provides a favorable trade-off between efficiency and data utility. The optimal algorithm consistently delivers the highest level of data quality, despite increased computational overhead. These findings indicate that the proposed model and algorithms provide an effective and practical solution for privacy preservation data publishing in real-world, content-based data environments.