Competition for the last-level cache (LLC) is a long-standing issue in multi-tenant cloud environments, often leading to severe performance interference among co-located virtual machines. LLC management in the cloud faces unique challenges, including unpredictable tenant workloads, misaligned performance metrics, and the need to ensure fairness under service level agreements (SLAs). Existing LLC allocation methods fall short in addressing these challenges. We present Cacheman, a comprehensive LLC management system designed from real-world cloud deployment experience. Cacheman introduces a novel gradient-based sharing mechanism for LLC ways, enabling smooth LLC allocation adjustments that simultaneously improve fairness and utilization efficiency. Its real-time allocation algorithm promptly detects and mitigates unfair LLC allocation, adapting to dynamic workloads with second-scale responsiveness. Additionally, Cacheman supports performance consistency for tenants running distributed applications by enforcing negotiated upper bounds on cache usage. Extensive experiments demonstrate that Cacheman effectively achieves its multi-dimensional goals, and long-term production deployment further shows that it significantly reduces SLA violations caused by LLC contention.

Cloud service providers increasingly adopt SmartNICs to offload data-plane services (e.g., DPDK and SPDK) and control-plane tasks (such as disk and NIC initialization). Our analysis of production environments reveals that data-plane services statically provision CPUs for peak load, resulting in 67.5% idle CPU cycles during 99% of their runtime in IaaS clouds, leading to wasted CPU resources. On the other hand, control-plane tasks fail to meet critical Service Level Objectives (SLOs), such as virtual machine startup time. Unfortunately, achieving control-plane SLO improvements through co-scheduling with idle data-plane services remains highly challenging, due to the combined effects of intrinsic scheduling latency and the substantial architectural complexity inherent to control-plane ecosystems.

Secret key establishment is a fundamental requirement for private communication between two wireless entities. An intriguing solution is to extract secret keys from the inherent randomness shared between them. Although several works have been done to extract secret keys from different kinds of mediums (e.g., RSSI, CSI, CIR), the efficiency and security problems are not fully solved. In this paper, we consider the problem of secret key establishment for wireless devices, and propose MAGIK, a secure and efficient scheme based on dynamic geoMAGnetic field in Indoor environment for Key establishment. We carefully study the feasibility of utilizing indoor geomagnetic field for key extraction through extensive measurements. Our results demonstrate that geomagnetic field has several dynamic properties, including space-varying, time-varying, sensitive to measurement device, and correlative between two observed points in proximity. We also optimize the key extraction process and present two rotation-angle-based quantification methods, which can achieve faster key generation rates and lower bit mismatching ratios. Besides, we build a prototype on commodity mobile devices, and evaluate its performance by conducting real-word experiments in indoor scenarios. The experiment results confirm that our system is efficient, in terms of key extraction rate, and robust in secret key establishment without requiring additional overhead on mobile devices.

The problem of dynamic spectrum redistribution has been extensively studied in recent years. Combinatorial auctions are believed to be among the most effective tools to allocate channels when bidders have diverse preferences on different bundles of channels. A great number of strategy-proof combinatorial auction mechanisms have been proposed to improve spectrum allocation efficiency by stimulating bidders to truthfully reveal their requested bundles and bidding values, which are bidders' private information. However, none of them has taken the bidders' privacy on both bidding values and bundles into account. In this paper, we consider the problem of privacy preservation on both bidders' bidding values and bundles, and present PICASSO, a Privacy-preservIng Combinatorial Auction mechaniSm for Spectrum redistributiOn. Specifically, PICASSO is designed based on BCP homomorphic cryptosystem by employing two non-colluding but semi-honest parties (an auctioneer and an auction issuer). Detailed analysis are given to demonstrate that PICASSO can guarantee strong protection for bidders' privacy on both bidding values and bundles without changing the traditional paradigm of communication in combinatorial spectrum auction. Furthermore, the results of comprehensive evaluations show that PICASSO achieves good performance in terms of spectrum redistribution with light overheads.

Tons of online user behavior data are being generated every day on the booming and ubiquitous Internet. Growing efforts have been devoted to mining the abundant behavior data to extract valuable information for research purposes or business interests. However, online users' privacy is thus under the risk of being exposed to third-parties. The last decade has witnessed a body of research works trying to perform data aggregation in a privacy-preserving way. Most of existing methods guarantee strong privacy protection yet at the cost of very limited aggregation operations, such as allowing only summation, which hardly satisfies the need of behavior analysis. In this paper, we propose a scheme PPSA, which encrypts users' sensitive data to prevent privacy disclosure from both outside analysts and the aggregation service provider, and fully supports selective aggregate functions for online user behavior analysis while guaranteeing differential privacy. We have implemented our method and evaluated its performance using a trace-driven evaluation based on a real online behavior dataset. Experiment results show that our scheme effectively supports both overall aggregate queries and various selective aggregate queries with acceptable computation and communication overheads.

Online user behavior analysis is becoming increasingly important, and offers valuable information to analysts for developing better e-commerce strategies. However, it also raises significant privacy concerns. Recently, growing efforts have been devoted to protecting the privacy of individuals while data aggregation is performed, which is a critical operation in behavior analysis. Unfortunately, existing methods allow very limited aggregation over user data, such as allowing only summation, which hardly satisfies the need of behavior analysis. In this paper, we propose a scheme PPSA, which encrypts users' sensitive data to prevent privacy leakage from both analysts and the aggregation service provider, and fully supports selective aggregate functions for differentially private data analysis. We have implemented our design and evaluated its performance using a trace-driven evaluation based on an online behavior dataset. Evaluation results show that our scheme effectively supports various selective aggregate queries with acceptable computation and communication overheads.

With the popularity of mobile wireless devices equipped with various kinds of sensing abilities, a new service paradigm named participatory sensing has emerged to provide users with brand new life experience. However, the wide application of participatory sensing has its own challenges, among which privacy and multimedia data quality preservations are two critical problems. Unfortunately, none of the existing work has fully solved the problem of privacy and quality preserving participatory sensing with multimedia data. In this paper, we propose SLICER, which is the first k-anonymous privacy preserving scheme for participatory sensing with multimedia data. SLICER integrates a data coding technique and message transfer strategies, to achieve strong protection of participants' privacy, while maintaining high data quality. Specifically, we study two kinds of data transfer strategies, namely transfer on meet up (TMU) and minimal cost transfer (MCT). For MCT, we propose two different but complimentary algorithms, including an approximation algorithm and a heuristic algorithm, subject to different strengths of the requirement. Furthermore, we have implemented SLICER and evaluated its performance using publicly released taxi traces. Our evaluation results show that SLICER achieves high data quality, with low computation and communication overhead.

The increasing popularity of smartphones, equipped with GPS, provides new opportunities for location-based service (LBS). Among all kinds of LBSs, targeted advertising based on users' locations takes great advantage of the rich location data to improve the accuracy of advertising and thus potentially increase the sellers' profits. However, location-based advertising (LBA) has raised significant privacy concerns, since the location information used in such kinds of services is private information, which the users may not be willing to expose. In this paper, we present POLA, which is a Privacy-preserving prOtocol for Location-based real-time Advertising. In this protocol, we not only preserve the privacy of the location data, we also take the values of advertisers into consideration which is also regarded as private information. We show the privacy-preserving properties of POLA in details. Furthermore, we have conducted simulations to evaluate the performance of POLA. Evaluation results show that POLA achieves privacy preserving LBA with relatively low overhead.

With the popularity of cloud computing, many companies would outsource their social network data to a cloud service provider, where privacy leaks have become a more and more serious problem. However, most of the previous studies have ignored an important fact, i.e., in real social networks, users possess various attributes and have the flexibility to decide which attributes of their profiles are sensitive attributes by themselves. These sensitive attributes of the users should be protected from being revealed when outsourcing a social network to a cloud service provider. In this paper, we consider the problem of resisting privacy attacks with neighborhood information of both network structure and labels of one-hop neighbors as background knowledge. To tackle this problem, we propose a Global Similarity-based Group Anonymization (GSGA) method to generate a anonymized social network while maintaining as much utility as possible. We also extensively evaluate our approach on both real data set and synthetic data sets. Evaluation results show that the social network anonymized by our approach can still be used to answer aggregation queries with high accuracy.

With the popularity of mobile wireless devices with various kinds of sensing abilities, a new service paradigm named Participatory Sensing has emerged to provide users with brand new life experience. However, the wide application of participatory sensing has its own challenges, among which privacy preservation and multimedia data participatory sensing are two critical problems. Unfortunately, none of the existing works has fully solved the problem of privacy preserving participatory sensing with multimedia data. In this paper, we propose SLICER, which is the first k-anonymous privacy preserving scheme for participatory sensing with multimedia data. SLICER integrates a data coding technique and message exchanging strategies, to achieve strong protection of participants' privacy, while maintaining high data accuracy. In addition, two slice transferring strategies are well designed for slice transfer to minimize the total transfer cost. Finally, we have implemented SLICER and evaluated its performance using publicly released taxi traces. Our evaluation results show that SLICER achieves high data accuracy, with low computation and communication overhead.