@article{jin_huang_zhang_dai_2023, title={On the Privacy Guarantees of Gossip Protocols in General Networks}, volume={10}, ISSN={["2327-4697"]}, DOI={10.1109/TNSE.2023.3247626}, abstractNote={Recently, the privacy guarantees of information dissemination protocols have attracted increasing research interests, among which the gossip protocols assume vital importance in various information exchange applications. In this article, we study the privacy guarantees of gossip protocols in general networks in terms of differential privacy and prediction uncertainty. First, lower bounds of the differential privacy guarantees are derived for gossip protocols in general networks in both synchronous and asynchronous settings. The prediction uncertainty of the source node given a uniform prior is also determined. For the private gossip algorithm, the differential privacy and prediction uncertainty guarantees are derived in closed forms in the asynchronous setting. Moreover, considering that these two metrics may be restrictive in some scenarios, the relaxed variants are proposed. It is found that source anonymity is closely related to some key network structure parameters in the general network setting. Then, we investigate information spreading in wireless networks with unreliable communications, and quantify the tradeoff between differential privacy guarantees and information spreading efficiency. Finally, considering that the attacker may not be present at the beginning of the information dissemination process, the scenario of delayed monitoring is studied and the corresponding differential privacy guarantees are evaluated.}, number={6}, journal={IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING}, author={Jin, Richeng and Huang, Yufan and Zhang, Zhaoyang and Dai, Huaiyu}, year={2023}, month={Nov}, pages={3114–3130} }
@article{pervej_jin_dai_2023, title={Resource Constrained Vehicular Edge Federated Learning With Highly Mobile Connected Vehicles}, volume={41}, ISSN={["1558-0008"]}, DOI={10.1109/JSAC.2023.3273700}, abstractNote={This paper proposes a vehicular edge federated learning (VEFL) solution, where an edge server leverages highly mobile connected vehicles’ (CVs’) onboard central processing units (CPUs) and local datasets to train a global model. Convergence analysis reveals that the VEFL training loss depends on the successful receptions of the CVs’ trained models over the intermittent vehicle-to-infrastructure (V2I) wireless links. Owing to high mobility, in the full device participation case (FDPC), the edge server aggregates client model parameters based on a weighted combination according to the CVs’ dataset sizes and sojourn periods, while it selects a subset of CVs in the partial device participation case (PDPC). We then devise joint VEFL and radio access technology (RAT) parameters optimization problems under delay, energy and cost constraints to maximize the probability of successful reception of the locally trained models. Considering that the optimization problem is NP-hard, we decompose it into a VEFL parameter optimization sub-problem, given the estimated worst-case sojourn period, delay and energy expense, and an online RAT parameter optimization sub-problem. Finally, extensive simulations are conducted to validate the effectiveness of the proposed solutions with a practical 5G new radio (5G-NR) RAT under a realistic microscopic mobility model.}, number={6}, journal={IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Pervej, Md Ferdous and Jin, Richeng and Dai, Huaiyu}, year={2023}, month={Jun}, pages={1825–1844} }
@article{jin_he_dai_2022, title={Communication Efficient Federated Learning With Energy Awareness Over Wireless Networks}, volume={21}, ISSN={["1558-2248"]}, DOI={10.1109/TWC.2021.3138394}, abstractNote={In federated learning (FL), reducing the communication overhead is one of the most critical challenges since the parameter server and the mobile devices share the training parameters over wireless links. With such consideration, we adopt the idea of SignSGD in which only the signs of the gradients are exchanged. Moreover, most of the existing works assume Channel State Information (CSI) available at both the mobile devices and the parameter server, and thus the mobile devices can adopt fixed transmission rates dictated by the channel capacity. In this work, only the parameter server side CSI is assumed, and channel capacity with outage is considered. In this case, an essential problem for the mobile devices is to select appropriate local processing and communication parameters (including the transmission rates) to achieve a desired balance between the overall learning performance and their energy consumption. Two optimization problems are formulated and solved, which optimize the learning performance given the energy consumption requirement, and vice versa. Furthermore, considering that the data may be distributed across the mobile devices in a highly uneven fashion in FL, a stochastic sign-based algorithm is proposed. Extensive simulations are performed to demonstrate the effectiveness of the proposed methods.}, number={7}, journal={IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS}, author={Jin, Richeng and He, Xiaofan and Dai, Huaiyu}, year={2022}, month={Jul}, pages={5204–5219} }
@article{yue_jin_wong_dai_2022, title={Communication-Efficient Federated Learning via Predictive Coding}, volume={16}, ISSN={["1941-0484"]}, DOI={10.1109/JSTSP.2022.3142678}, abstractNote={Federated learning can enable remote workers to collaboratively train a shared machine learning model while allowing training data to be kept locally. In the use case of wireless mobile devices, the communication overhead is a critical bottleneck due to limited power and bandwidth. Prior work has utilized various data compression tools such as quantization and sparsification to reduce the overhead. In this paper, we propose a predictive coding based compression scheme for federated learning. The scheme has shared prediction functions among all devices and allows each worker to transmit a compressed residual vector derived from the reference. In each communication round, we select the predictor and quantizer based on the rate–distortion cost, and further reduce the redundancy with entropy coding. Extensive simulations reveal that the communication cost can be reduced up to 99% with even better learning performance when compared with other baseline methods.}, number={3}, journal={IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING}, author={Yue, Kai and Jin, Richeng and Wong, Chau-Wai and Dai, Huaiyu}, year={2022}, month={Apr}, pages={369–380} }
@article{he_li_jin_dai_2022, title={Delay-Optimal Coded Offloading for Distributed Edge Computing in Fading Environments}, volume={21}, ISSN={["1558-2248"]}, DOI={10.1109/TWC.2022.3187427}, abstractNote={The rapid growth in scale and complexity of mobile applications fosters the development of the coded edge computing paradigm. By exploiting the redundancy in the encoded subtasks, coded edge computing enables collaborative transmission of multiple edge nodes and is promising for distributed computing in wireless fading environments. Nonetheless, to the best of our knowledge, due to challenges arising from the selection of the coding parameters, offloading strategy design for coded edge computing in general fading environments still remains open. With this consideration, the coded offloading problem is studied in this work and a delay-optimal coded offloading scheme is proposed. In particular, when the offloaded tasks are encoded by $(k,r)$ linear codes, transmission diversity gains can be obtained by performing edge node selection to mitigate fading. However, the corresponding optimization problem turns out to be a highly non-trivial non-linear mixed-integer programming. To this end, through in-depth analysis based on order statistics, it is found that the average processing delay of the offloaded tasks admits a favorable $V$ -structure with respect to the coding parameter $r$ , under arbitrary fading distribution. This key theoretic result allows us to efficiently solve the original problem using monotonic optimization. Simulations are conducted to validate our analysis and corroborate the effectiveness of the proposed scheme.}, number={12}, journal={IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS}, author={He, Xiaofan and Li, Tianheng and Jin, Richeng and Dai, Huaiyu}, year={2022}, month={Dec}, pages={10796–10808} }
@article{he_jin_dai_2022, title={Multi-Hop Task Offloading With On-the-Fly Computation for Multi-UAV Remote Edge Computing}, volume={70}, ISSN={["1558-0857"]}, DOI={10.1109/TCOMM.2021.3129902}, abstractNote={The dramatic growth in computing capability and the inherent mobility of the unmanned aerial vehicles (UAVs) foster the recent surge of interests in incorporating UAVs into edge computing systems to facilitate on-demand deployment and extended coverage. Nonetheless, due to the limited communication capability of the UAVs, single-UAV edge computing systems may still be incompetent when serving remote users. Although the traditional multi-UAV relay network can be a viable solution, it fails to exploit the computing capability of the UAVs. With this consideration, a multi-hop task offloading with on-the-fly computation scheme is proposed in this work to enable a more powerful multi-UAV remote edge computing network. To solve the corresponding joint resource allocation and deployment problem, two efficient algorithms are proposed. One of them can find the global optimal strategy in a special case, while the other can obtain a good local optimal strategy in the general cases. Both algorithms have a complexity only linear in the number of UAVs and admit distributed implementation. In addition to analysis, numerical results are provided to corroborate the effectiveness of the proposed scheme.}, number={2}, journal={IEEE TRANSACTIONS ON COMMUNICATIONS}, author={He, Xiaofan and Jin, Richeng and Dai, Huaiyu}, year={2022}, month={Feb}, pages={1332–1344} }
@article{he_jin_dai_2021, title={Joint Service Placement and Resource Allocation for Multi-UAV Collaborative Edge Computing}, ISSN={["1525-3511"]}, DOI={10.1109/WCNC49053.2021.9417565}, abstractNote={Driven by the burgeoning development of unmanned aerial vehicle (UAV) technology, the recently advocated multi-UAV edge computing paradigm is anticipated to greatly enhance the coverage and on-demand deployment capability of the edge networks. One of the prominent advantage of this paradigm is to allow the UAVs to participate in the edge computing process by executing some computing tasks at their onboard processors. To this end, a key prerequisite is that the corresponding computing services must be placed onboard beforehand. Nonetheless, unlike its counterpart for conventional ground edge systems, the service placement issue in multi-UAV edge computing systems remains much less explored. To the best of our knowledge, this work is among the first to consider the joint service placement and resource allocation problem for multi-UAV edge computing. Due to the mutual influence between service placement and resource allocation, this problem turns out to be a computationally intractable mixed-integer nonlinear programming. Fortunately, through our analysis, it is found that this problem can be divided into two subproblems that are submodular and convex, respectively. Based on this observation and the general alternative optimization framework, an efficient joint service placement and resource allocation scheme that can find a reasonably good solution with only a linear complexity is proposed. In addition to the analysis, simulations are conducted to validate the effectiveness of the proposed scheme.}, journal={2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)}, author={He, Xiaofan and Jin, Richeng and Dai, Huaiyu}, year={2021} }
@article{jin_he_dai_2021, title={Minimizing the Age of Information in the Presence of Location Privacy-Aware Mobile Agents}, volume={69}, ISSN={["1558-0857"]}, DOI={10.1109/TCOMM.2020.3035394}, abstractNote={The recent advances in wireless sensor networks and sensing techniques enable various time-sensitive applications that require timely exchange of updates between a Base Station (BS) and ground terminals. In practice, the ground terminals may not be able to communicate with the BS directly due to constraints in transmit power and communication capability, and mobile agents are commonly employed to help collect and deliver the updates. In particular, the emerging mobile crowd sensing (MCS) provides an appealing cost-effective paradigm for such employment. However, in this case, the mobile agents are required to share their locations with the ground terminals and the BS, which incurs location privacy concerns and may deter them from participating in the information delivery process. With this consideration, a location privacy-aware payment mechanism, which can stimulate the mobile agents to report their locations with differential privacy levels desired by the BS, is proposed. Furthermore, considering that the BS usually has a limited budget, it is essential to properly select the set of mobile agents to perform the information collection tasks. Therefore, a cost-efficient mobile agent selection algorithm is proposed. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method.}, number={2}, journal={IEEE TRANSACTIONS ON COMMUNICATIONS}, author={Jin, Richeng and He, Xiaofan and Dai, Huaiyu}, year={2021}, month={Feb}, pages={1053–1067} }
@article{huang_jin_dai_2020, title={Differential Privacy and Prediction Uncertainty of Gossip Protocols in General Networks}, ISSN={["2576-6813"]}, DOI={10.1109/GLOBECOM42002.2020.9322558}, abstractNote={Recent advances in social media and information technology have enabled much faster dissemination of information, while at the same time raise concerns about privacy leakage after various privacy breaches. Therefore, the privacy guarantees of information dissemination protocols have attracted increasing research interests, among which the gossip protocols assume vital importance in various information exchange applications. Very recently, the rigorous framework of differential privacy has been introduced to measure the privacy guarantees of gossip protocols in the simplified complete network scenario. In this work, we extend the study to general networks. First, lower bounds of the differential privacy guarantees are derived for the gossip protocols in general networks in both synchronous and asynchronous settings. The prediction uncertainty of the source node given a uniform prior is also determined. It is found that source anonymity is closely related to some key network structure parameters in the general network setting. Then, we investigate information spreading in wireless networks with unreliable communications, and quantity the tradeoff between differential privacy guarantees and information spreading efficiency. Finally, considering that the attacker may not be present in the beginning of the information dissemination process, the scenario of delayed monitoring is studied and the corresponding differential privacy guarantees are evaluated.}, journal={2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)}, author={Huang, Yufan and Jin, Richeng and Dai, Huaiyu}, year={2020} }
@article{he_jin_dai_2020, title={Joint Power and Deployment Optimization for Multi-UAV Remote Edge Computing}, ISSN={["2576-6813"]}, DOI={10.1109/GLOBECOM42002.2020.9348243}, abstractNote={Driven by the dramatic growth in computing capability and the inherent mobility of the unmanned aerial vehicles (UAVs), the recently advocated UAV edge computing paradigm is expected to enhance the coverage and the on-demand deployment capability of existing terrestrial edge computing systems. Nonetheless, due to the limited onboard resource of the UAV, single- UAV edge computing systems may still be incompetent when serving remote users. Although using multiple UAVs to form a traditional relay network is a viable solution to remote edge computing, it fails to exploit the computing capability of the UAVs. This entails a pressing need to develop multi-UAV remote edge computing mechanisms that allow the UAVs to handle part of the computation tasks using their local processors while conducting multi-hop computation task offloading. To achieve the best performance in such cases, the UAVs have to properly split their power budget for communication and computation and also move to suitable service locations. Nonetheless, finding the optimal UAV power allocation and deployment turns out to be an intractable high-dimensional monotonic optimization problem, even for a mild number of UAVs. To overcome this challenge, a more efficient algorithm that has a complexity only linear in the number of UAVs is developed by exploiting the special structure of this problem. In addition to analysis, numerical results are provided to validate the effectiveness of the proposed scheme.}, journal={2020 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)}, author={He, Xiaofan and Jin, Richeng and Dai, Huaiyu}, year={2020} }
@article{he_jin_dai_2020, title={Peace: Privacy-Preserving and Cost-Efficient Task Offloading for Mobile-Edge Computing}, volume={19}, ISSN={["1558-2248"]}, DOI={10.1109/TWC.2019.2958091}, abstractNote={The limited information processing capability and battery life of mobile devices is becoming a bottleneck in delivering more advanced and high-quality services to the customers. To address this problem, the recently advocated mobile-edge computing (MEC) architecture is promising, where the essential idea is to bring the computation resource to the network edge and allow users to wirelessly offload resource demanding computation tasks to the nearby MEC servers for potentially faster execution and lower battery consumption. Nonetheless, the existing understanding of the privacy aspect of MEC is still far from complete. In this work, a user presence inference attack that invades user privacy by exploiting the feature tasks offloaded from users is identified for MEC. Existing privacy-preserving techniques developed for other applications cannot be applied to defeat this attack in MEC, as they may disrupt the optimal task offloading scheduling and cause severe degradation in user experience. With this consideration, a novel privacy-preserving and cost-efficient (PEACE) task offloading scheme that can preserve user privacy while still ensure the best possible user experience is developed in this work based on the generic Lyapunov optimization framework. The effectiveness of the proposed scheme is validated through both analysis and simulations.}, number={3}, journal={IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS}, author={He, Xiaofan and Jin, Richeng and Dai, Huaiyu}, year={2020}, month={Mar}, pages={1814–1824} }
@article{he_jin_dai_2020, title={Physical-Layer Assisted Secure Offloading in Mobile-Edge Computing}, volume={19}, ISSN={["1558-2248"]}, DOI={10.1109/TWC.2020.2979456}, abstractNote={The wireless offloading feature of the recently advocated mobile-edge computing (MEC) imposes a risk of disclosing private user data to eavesdroppers. Physical-layer security approaches that are built on information theoretic methods can be applied to defend eavesdropping in MEC. Nonetheless, directly incorporating existing physical-layer security technique may introduce extra energy and delay costs to the resource-limited mobile device and thus substantially disrupt the users’ offloading decisions. To fulfill effective secure offloading in MEC, there is a compelling need to properly optimize existing physical-layer security techniques and develop new offloading schemes accordingly. With this consideration, a novel physical-layer assisted secure offloading scheme is proposed in this work, in which the edge server proactively broadcasts jamming signals to impede eavesdropping and leverages full-duplex communication technique to effectively suppress the self-interference. Finding the optimal jamming signal and the corresponding optimal offloading ratio turns out to be a challenging bilevel optimization problem. The special structure of the secure offloading problem is exploited to develop efficient offloading algorithms. Numerical results are presented to validate the effectiveness of the proposed scheme.}, number={6}, journal={IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS}, author={He, Xiaofan and Jin, Richeng and Dai, Huaiyu}, year={2020}, month={Jun}, pages={4054–4066} }
@article{he_jin_dai_2019, title={Camouflaging Mobile Primary Users in Database-Driven Cognitive Radio Networks}, volume={8}, ISSN={["2162-2345"]}, DOI={10.1109/LWC.2018.2846621}, abstractNote={The ever-increasing wireless demand has led to the opening of the 3.5 GHz spectrum band to commercial applications and the enforcement of database-driven cognitive radio networks. Along with its advantages, this technological amendment raises new privacy concerns, especially on the primary users (PUs) that are sensitive federal and military devices. Existing studies mainly focus on static PUs while the privacy issue of mobile PUs still remains unresolved. In this letter, a novel scheme that can camouflage mobile PUs through generating low-cost fake trajectories in the database is developed, and its effectiveness is validated by numerical results.}, number={1}, journal={IEEE WIRELESS COMMUNICATIONS LETTERS}, author={He, Xiaofan and Jin, Richeng and Dai, Huaiyu}, year={2019}, month={Feb}, pages={21–24} }
@article{he_jin_dai_2019, title={Deep PDS-Learning for Privacy-Aware Offloading in MEC-Enabled IoT}, volume={6}, ISSN={["2327-4662"]}, DOI={10.1109/JIOT.2018.2878718}, abstractNote={The rapid uptake of Internet-of-Things (IoT) devices imposes an unprecedented pressure for data communication and processing on the backbone network and the central cloud infrastructure. To overcome this issue, the recently advocated mobile-edge computing (MEC)-enabled IoT is promising. Meanwhile, driven by the growing social awareness of privacy, significant research efforts have been devoted to relevant issues in IoT; however, most of them mainly focus on the conventional cloud-based IoT. In this paper, a new privacy vulnerability caused by the wireless offloading feature of MEC-enabled IoT is identified. To address this vulnerability, an effective privacy-aware offloading scheme is developed based on a newly proposed deep post-decision state (PDS)-learning algorithm. By exploiting extra prior information, the proposed deep PDS-learning algorithm allows the IoT devices to learn a good privacy-aware offloading strategy much faster than the conventional deep ${Q}$ -network. Theoretic analysis and numerical results are provided to corroborate the correctness and the effectiveness of the proposed algorithm.}, number={3}, journal={IEEE INTERNET OF THINGS JOURNAL}, author={He, Xiaofan and Jin, Richeng and Dai, Huaiyu}, year={2019}, month={Jun}, pages={4547–4555} }
@article{jin_he_dai_2019, title={On the Security-Privacy Tradeoff in Collaborative Security: A Quantitative Information Flow Game Perspective}, volume={14}, ISSN={["1556-6021"]}, DOI={10.1109/TIFS.2019.2914358}, abstractNote={To contest the rapidly developing cyber-attacks, numerous collaborative security schemes, in which multiple security entities can exchange their observations and other relevant data to achieve more effective security decisions, are proposed and developed in the literature. However, the security-related information shared among the security entities may contain some sensitive information and such information exchange can raise privacy concerns, especially when these entities belong to different organizations. With such consideration, the interplay between the attacker and the collaborative entities is formulated as Quantitative Information Flow (QIF) games, in which the QIF theory is adapted to measure the collaboration gain and the privacy loss of the entities in the information sharing process. In particular, three games are considered, each corresponding to one possible scenario of interest in practice. Based on the game-theoretic analysis, the expected behaviors of both the attacker and the security entities are obtained. In addition, the simulation results are presented to validate the analysis.}, number={12}, journal={IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY}, author={Jin, Richeng and He, Xiaofan and Dai, Huaiyu}, year={2019}, month={Dec}, pages={3273–3286} }
@article{jin_he_dai_2018, title={Collaborative IDS Configuration: A Two-Layer Game-Theoretic Approach}, volume={4}, ISSN={["2332-7731"]}, DOI={10.1109/TCCN.2018.2856207}, abstractNote={To cope with the increasingly sophisticated intrusions, collaborative intrusion detection systems (CIDSs) are proposed in the literature. In particular, intrusion detection systems (IDSs) in collaboration can dynamically share available computational resources among themselves to enhance the overall detection performance. However, due to resource limitation, it is infeasible for the IDSs to respond to all the intrusion detection requests from their collaborative peers. In the meantime, obtaining the optimal IDS configuration in CIDSs is far from trivial. With such consideration, the collaborative IDS configuration problem is formulated as a two-layer stochastic game (SG). To solve this two-layer SG, a centralized Vickrey–Clarke–Groves auction based collaboration scheme and a distributed game-theoretic incentive mechanism are proposed in this paper. The effectiveness of the proposed schemes is validated through both analysis and numerical experiments. The proposed approach can be applied to more general collaborative settings.}, number={4}, journal={IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING}, author={Jin, Richeng and He, Xiaofan and Dai, Huaiyu}, year={2018}, month={Dec}, pages={803–815} }
@article{he_jin_dai_2018, title={Leveraging Spatial Diversity for Privacy-Aware Location-Based Services in Mobile Networks}, volume={13}, ISSN={["1556-6021"]}, DOI={10.1109/tifs.2018.2797023}, abstractNote={While providing unprecedented convenience to people’s daily life, location-based services (LBSs) may cause serious concerns on users’ location privacy, when the system is compromised. Although various location privacy protection mechanisms have been developed for LBSs, the ambient physical environment often imposes some fundamental limitations on their performances. As a result, mobile users may experience a spatial diversity in the achievable location privacy when traveling along their routes. However, to the best of our knowledge, an appropriate location privacy metric that can capture the influence of the ambient environment is still missing in the literature. Also, none of the existing location privacy protection methods can properly leverage such spatial diversity. With this consideration, new ambient environment-dependent location privacy metrics are proposed in this paper, together with a stochastic model that can capture their spatial variations along the user’s route. Based on this modeling, a new optimal stopping-based LBS access scheme that allows mobile users to fully leverage the spatial diversity and achieve a substantially better performance is developed. The effectiveness of the proposed scheme is corroborated by both numerical results and simulations over real-world road maps.}, number={6}, journal={IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY}, author={He, Xiaofan and Jin, Richeng and Dai, Huaiyu}, year={2018}, month={Jun}, pages={1524–1534} }
@inproceedings{he_islam_jin_dai_2017, title={Foresighted deception in dynamic security games}, DOI={10.1109/icc.2017.7996811}, abstractNote={Deception has been widely considered in literature as an effective means of enhancing security protection when the defender holds some private information about the ongoing rivalry unknown to the attacker. However, most of the existing works on deception assume static environments and thus consider only myopic deception, while practical security games between the defender and the attacker may happen in dynamic scenarios. To better exploit the defender's private information in dynamic environments and improve security performance, a stochastic deception game (SDG) framework is developed in this work to enable the defender to conduct foresighted deception. To solve the proposed SDG, a new iterative algorithm that is provably convergent is developed. A corresponding learning algorithm is developed as well to facilitate the defender in conducting foresighted deception in unknown dynamic environments. Numerical results show that the proposed foresighted deception can offer a substantial performance improvement as compared to the conventional myopic deception.}, booktitle={2017 ieee international conference on communications (icc)}, author={He, X. F. and Islam, M. M. and Jin, R. C. and Dai, H. Y.}, year={2017} }
@inproceedings{he_liu_jin_dai_2017, title={Privacy-aware offloading in mobile-edge computing}, DOI={10.1109/glocom.2017.8253985}, abstractNote={Recently, mobile-edge computing (MEC) emerges as a promising paradigm to enable computation intensive and delay-sensitive applications at resource limited mobile devices by allowing them to offload their heavy computation tasks to nearby MEC servers through wireless communications. A substantial body of literature is devoted to developing efficient scheduling algorithms that can adapt to the dynamics of both the system and the ambient wireless environments. However, the influence of these task offloading schemes to the mobile users' privacy is largely ignored. In this work, two potential privacy issues induced by the wireless task offloading feature of MEC, location privacy and usage pattern privacy, are identified. To address these two privacy issues, a constrained Markov decision process (CMDP) based privacy-aware task offloading scheduling algorithm is proposed, which allows the mobile device to achieve the best possible delay and energy consumption performance while maintain a pre-specified level of privacy. Numerical results are presented to corroborate the effectiveness of the proposed algorithm.}, booktitle={Globecom 2017 - 2017 ieee global communications conference}, author={He, X. F. and Liu, J. and Jin, R. C. and Dai, H. Y.}, year={2017} }
@article{jin_he_dai_dutta_ning_2017, title={Towards Privacy-Aware Collaborative Security: A Game-Theoretic Approach}, DOI={10.1109/pac.2017.32}, abstractNote={With the rapid development of sophisticated attack techniques, individual security systems that base all of their decisions and actions of attack prevention and response on their own observations and knowledge become incompetent. To cope with this problem, collaborative security in which a set of security entities are coordinated to perform specific security actions is proposed in literature. In collaborative security schemes, multiple entities collaborate with each other by sharing threat evidence or analytics to make more effective decisions. Nevertheless, the anticipated information exchange raises privacy concerns, especially for those privacy-sensitive entities. In order to obtain a quantitative understanding of the fundamental tradeoff between the effectiveness of collaboration and the entities' privacy, a repeated two-layer single-leader multi-follower game is proposed in this work. Based on our game-theoretic analysis, the expected behaviors of both the attacker and the security entities are derived and the utility-privacy tradeoff curve is obtained. In addition, the existence of Nash equilibrium (NE) for the collaborative entities is proven, and an asynchronous dynamic update algorithm is proposed to compute the optimal collaboration strategies of the entities. Furthermore, the existence of Byzantine entities is considered and its influence is investigated. Finally, simulation results are presented to validate the analysis.}, journal={2017 1ST IEEE SYMPOSIUM ON PRIVACY-AWARE COMPUTING (PAC)}, author={Jin, Richeng and He, Xiaofan and Dai, Huaiyu and Dutta, Rudra and Ning, Peng}, year={2017}, pages={72–83} }
@inproceedings{jin_he_dai_2016, title={Collaborative IDS configuration: A two-layer game-theoretical approach}, DOI={10.1109/glocom.2016.7841671}, abstractNote={As information systems become ubiquitous, Intrusion Detection Systems (IDSs) have assumed increasing importance. As a result, substantial amount of research efforts have been devoted to developing various intrusion detection algorithms. However, there is still no single detection algorithm that can catch all possible attacks. On the other hand, it is infeasible for practical IDSs to run all the detection algorithms simultaneously due to resource limitation, leaving potential opportunities for the adversaries to explore. This resource scarcity problem becomes more severe when the system is in an ill state (e.g., partially compromised). Enabling collaboration among multiple IDSs may be a viable way to mitigate this problem. Particularly, IDSs in the healthy state can share some of their idle computational resources to those in ill states, so as to improve the overall intrusion detection performance. Considering this, the collaborative IDS configuration problem is formulated as a two-layer stochastic game (SG) in this work and a new algorithm is proposed to solve this two-layer SG. Simulation results show that the proposed algorithm can provide an effective collaborative configuration scheme, leading to significant detection performance gain. Some performance analysis has also been given, and the conditions under which there is a guaranteed improvement in expected system performance have been derived.}, booktitle={2016 ieee global communications conference (globecom)}, author={Jin, R. C. and He, X. F. and Dai, H. Y.}, year={2016} }