Logging is a common practice in software development, widely used for debugging, testing, and performance monitoring. However, recording sensitive user data can introduce severe privacy risks. Past incidents involving leaked logs have prompted platforms such as Android to publish strict guidelines discouraging developers from logging personally identifiable information (PII) and other ''linkable'' or ''ambiguous'' data unless strictly required for core functionality. To evaluate real-world compliance, we examined the logging practices of 500 Android applications across six categories. Our findings reveal that 264 apps contain logging violations, from which we identified 864 instances of sensitive data exposure. Notably, 54% of these violations stem from debugging logs that should have been removed before release. The recorded data includes PII such as email addresses and phone numbers, as well as linkable information such as shopping history, health records, and private messages each in direct violation of Android's privacy guidelines. Moreover, removing these logging statements from application source code does not affect app functionality, raising questions about their necessity. Our analysis shows that most violations originate from debugging practices, third-party analytics tracking, and HTTP request logging. Further, by applying a large language model (LLM) to inspect an additional set of 300 applications, we found 240 apps exhibiting sensitive data logging violations. We also discovered that many apps share log data with third-party services, often contradicting their own privacy policies. To mitigate these risks, we provide practical recommendations for both app developers and mobile platforms to enforce responsible and privacy-preserving logging practices.

In blockchains, mempool controls transaction flow before consensus, denial of whose service hurts the health and security of blockchain networks. This paper presents MPFUZZ, the first mempool fuzzer to find asymmetric DoS bugs by exploring the space of symbolized mempool states and optimistically estimating the promisingness of an intermediate state in reaching bug oracles. Compared to the baseline blockchain fuzzers, MPFUZZ achieves a > 100× speedup in finding known DETER exploits. Running MPFUZZ on major Ethereum clients leads to discovering new mempool vulnerabilities, which exhibit a wide variety of sophisticated patterns, including stealthy mempool eviction and mempool locking. Rule-based mitigation schemes are proposed against all newly discovered vulnerabilities.

A mempool is a security-critical subsystem in a public blockchain. Recent mempool attacks, notably asymmetric DoS, have shown their ability to severely damage the Ethereum network. This paper tackles the open research problem of designing principled and non-intrusive defenses against asymmetric mempool DoSes with provable security. It presents the first mempool economic-security definitions based on mempool-observable conditions. It then presents SAFERAD, a framework of secure mempool designs with provable security against asymmetric DoSes. To defend against dual attacks by evicting and locking a victim mempool, SAFERAD adopts a non-trivial design of enforcing an upper bound of the attack damage under the locking attacks and a lower bound of the attack cost under the eviction attacks. With a prototype implementation on Geth and evaluation under real transaction traces, the results show SAFERAD has low overhead in latency and block revenue, implying non-intrusiveness and practicality.

In Web 3.0, an emerging paradigm of building decentralized applications or DApps is off-chain message signing, which has advantages in performance, cost efficiency, and usability compared to conventional transaction-signing schemes. However, message signing burdens DApp developers with extra coding complexity and message designing, leading to new security risks.

Ensuring global food security depends heavily on attaining and sustaining high maize yields. Effective N fertilization management and precise predictions of maize yields, however, require accurate and timely estimation of leaf chlorophyll content (LCC). In this study, we determined the optimal spectral features for predicting LCC in maize by comparing spectral indices and wavelet features. The robustness of the wavelet functions in estimating maize LCC was evaluated, and the results showed that LCC was strongly correlated with the wavelet coefficient between 400 and 800 nm, located at higher scales (9 and 10). The best wavelet function for estimating LCC was the Mexican hat (Mexh) continuous wavelet transform (CWT) (W718, S9). Compared with the currently accepted best spectral index model (mND705,${R}^{2} = 0.80$–0.95), the LCC estimation model based on the CWT wavelet function (Mexh,${R}^{2} = 0.90$–0.98) was more accurate. The newly developed model was validated using two independent datasets, from 2017 to 2018, yielding root mean squared errors of 2.35 and$2.39~\mu $g/cm2, respectively. The relative errors of LCC estimation obtained by the new model were 3.70% and 3.62%, respectively. Validations based on the PROSPECT model confirmed the robustness and stability of the CWT Mexh function compared to the best-performing spectral indices. In conclusion, the higher estimation accuracy of the Mexh function-based wavelet transform across growth stages, leaf layers, locations, and varieties demonstrated the universality and stability of the wavelet transform approach in estimating maize LCC.

Android, being one of the most widely used mobile systems, is facing pressing threats from malware. Despite the effectiveness of Android malware detection (AMD) systems, they are still vulnerable to state-of-the-art adversarial attacks. Existing defense methods require the knowledge of target adversaries, such as attack algorithms or obfuscation strategies, which is impractical in real-world scenarios. Additionally, these approaches may adversely affect the performance of the detection model and fail to defend against problem-space attacks, which not only deceive the detection models but also generate executable adversarial software. To address this research gap, we propose a novel interpretable Android guard system, named IADGuard, to help AMD defend against attacks. IADGuard first designs a novel graph explainable method, AGExplainer, to identify suspicious functions and invocations in adversarial malware. With the guidance of AGExplainer, IADGuard develops a rectifier to reverse adversarial modifications on apps’ function invocation relations, which facilitates the detection of adversarial malware by victim AMD. It is noteworthy that IADGuard requires zero knowledge of adversarial models and victim models, thereby preserves the performance of victim AMD. We validate IADGuard over three state-of-the-art problem space attacks that modify apps’ function invocation relations to deceive victim AMD. Experimental results show that IADGuard achieves over 90.5% defense success rate, i.e., helps victim AMD identify adversarial malware. Furthermore, AGExplainer surpasses representative interpreters in identifying essential modifications, helps IADGuard reduce false positives to 1.5%, and improves the detection efficiency by up to 10.4 times.

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions.

Upgradeable smart contracts (USCs) have been widely adopted to enable modifying deployed smart contracts. While USCs bring great flexibility to developers, improper usage might introduce new security issues, potentially allowing attackers to hijack USCs and their users. In this paper, we conduct a large-scale measurement study to characterize USCs and their security implications in the wild. We summarize six commonly used USC patterns and develop a tool, USCDetector, to identify USCs without needing source code. Particularly, USCDetector collects various information such as bytecode and transaction information to construct upgrade chains for USCs and disclose potentially vulnerable ones. We evaluate USCDetector using verified smart contracts (i.e., with source code) as ground truth and show that USCDetector can achieve high accuracy with a precision of 96.26%. We then use USCDetector to conduct a large-scale study on Ethereum, covering a total of 60,251,064 smart contracts. USCDetecor constructs 10,218 upgrade chains and discloses multiple real-world USCs with potential security issues.

DEX, or decentralized exchange, is a prominent class of decentralized finance (DeFi) applications on blockchains, attracting a total locked value worth tens of billions of USD today.This paper presents the first large-scale empirical study that uncovers unfair trades on popular DEX services on Ethereum and Binance Smart Chain (BSC). By joining and analyzing 60 million transactions, we find 671, 400 unfair trades on all six measured DEXes, including Uniswap, Balancer, and Curve. Out of these unfair trades, we attribute 55, 000 instances, with high confidence, to token thefts that cause a value loss of more than 3.88 million USD. Furthermore, the measurement study uncovers previously unknown causes of extractable value and real-world adaptive strategies to these causes. Finally, we propose countermeasures to redesign secure DEX protocols and to harden deployed services against the discovered security risks.

Today's blockchain systems store detailed runtime information in the format of transactions and blocks, which are valuable not only to understand the finance of blockchain-based ecosystems but also to audit the security of on-chain applications. However, exploring this blockchain “big data” is challenging due to data heterogeneity and the huge amount. Existing blockchain exploration techniques are either incomplete or inefficient, making them inapt in time-sensitive applications. This paper presents ${\sf BlockExplorer}$ , an efficient and flexible blockchain exploration system for Ethereum. ${\sf BlockExplorer}$ builds on a master-slave architecture, where the master partitions all blocks into multiple non-overlapped sets and each slave simultaneously processes Ethereum big data based on a set of blocks. ${\sf BlockExplorer}$ implements a transaction-based partitioning approach to address load balance among slaves, and a code instrumentation approach to acquire complete Ethereum big data. The evaluation shows that ${\sf BlockExplorer}$ accelerates the data acquisition performance of the state-of-the-art by 4.1×, while the workload difference among slaves is up to 18%. To demonstrate the application of ${\sf BlockExplorer}$ , we develop three apps upon ${\sf BlockExplorer}$ to detect real-life attacks against Ethereum and show that our apps can detect attacks in a large range of blocks (e.g., ten million) within a short time (e.g., multiple hours).

This paper presentsiBatch, a middleware system running on top of an operational Ethereum network to enable secure batching of smart-contract invocations against an untrusted relay server off-chain.iBatchdoes so at a low overhead by validating the server's batched invocations in smart contracts without additional states of user nonces. TheiBatchmechanism supports a variety of policies, ranging from conservative to aggressive batching, and can be configured adaptively to the current workloads.iBatchautomatically rewrites smart contracts to integrate with legacy applications and support large-scale deployment. We built an evaluation platform for fast and cost-accurate transaction replaying and constructed real transaction benchmarks on popular Ethereum applications. With a functional prototype ofiBatch, we conduct extensive cost evaluations, which showsiBatchsaves$14.6\%\sim {}59.1\%$Gas cost per invocation with a moderate 2-minute delay and$19.06\%\sim {}31.52\%$Ether cost per invocation with a delay of$0.26\sim {}1.66$blocks.

As transaction fees skyrocket today, blockchains become increasingly expensive, hurting their adoption in broader applications. This work tackles the saving of transaction fees for economic blockchain applications. The key insight is that other than the existing "default'' mode to execute application logic fully on-chain, i.e., in smart contracts, and in fine granularity, i.e., user request per transaction, there are alternative execution modes with advantages in cost-effectiveness. On Ethereum, we propose a holistic middleware platform supporting flexible and secure transaction executions, including off-chain states and batching of user requests. Furthermore, we propose control-plane schemes to adapt the execution mode to the current workload for optimal runtime cost. We present a case study on the institutional accounts (e.g., coinbase.com) intensively sending Ether on Ethereum blockchains. By collecting real-life transactions, we construct workload benchmarks and show that our work saves 18%\sim 47%18%-47% per invocation than the default baseline while introducing 1.81%\sim 16.59%1.81%-16.59% blocks delay.

On an Ethereum node, txpool (a.k.a. mempool) is a buffer storing unconfirmed transactions and controls what downstream services can see, such as mining and transaction propagation. This work presents the first security study on Ethereum txpool designs.

Ethereum relies on a peer-to-peer overlay network to propagate information. The knowledge of Ethereum network topology holds the key to understanding Ethereum's security, availability, and user anonymity. However, an Ethereum network's topology is stored in individual nodes' internal routing tables, measuring which poses challenges and remains an open research problem in the existing literature.

This paper presents iBatch, a middleware system running on top of an operational Ethereum network to enable secure batching of smart-contract invocations against an untrusted relay server off-chain. iBatch does so at a low overhead by validating the server's batched invocations in smart contracts without additional states. The iBatch mechanism supports a variety of policies, ranging from conservative to aggressive batching, and can be configured adaptively to the current workloads. iBatch automatically rewrites smart contracts to integrate with legacy applications and support large-scale deployment.

Feeding external data to a blockchain, a.k.a. data feed, is an essential task to enable blockchain interoperability and support emerging cross-domain applications. Given the data-intensive nature of real-life feeds (e.g., high-frequency price updates) and the high cost of using blockchain, namely Gas, it is imperative to reduce the Gas cost of data feeds. Motivated by the constant-changing workloads in financial applications, this work aims at designing a dynamic, workload-aware approach for Gas cost optimization. This design space is understudied in existing blockchain research which has so far focused on static data placement.

When performing any analysis task, some information may be leaked or scattered among individuals who may not willing to share their information (e.g., number of individual's friends and who they are). Secure multi-party computation (MPC) allows individuals to jointly perform any computation without revealing each individual's input. Here, we present two novel secure frameworks which allow node to securely compute its clustering coefficient, which we evaluate the trade off between efficiency and security of several proposed instantiations. Our results show that the cost for secure computing highly depends on network structure.

Blockchain technology has attracted much attention due to the great success of the cryptocurrencies. Owing to its immutability property and consensus protocol, blockchain offers a new solution for trusted storage and computation services. To scale up the services, prior research has suggested a hybrid storage architecture, where only small meta-data are stored onchain and the raw data are outsourced to off-chain storage. To protect data integrity, a cryptographic proof can be constructed online for queries over the data stored in the system. However, the previous schemes only support simple key-value queries. In this paper, we take the first step toward studying authenticated range queries in the hybrid-storage blockchain. The key challenge lies in how to design an authenticated data structure (ADS) that can be efficiently maintained by the blockchain, in which a unique gas cost model is employed. By analyzing the performance of the existing techniques, we propose a novel ADS, called GEM2-tree, which is not only gas-efficient but also effective in supporting authenticated queries. To further reduce the ADS maintenance cost without sacrificing much the query performance, we also propose an optimized structure, GEM2*-tree, by designing a two-level index structure. Theoretical analysis and empirical evaluation validate the performance of the proposed ADSs.

There has been a dramatic increase in the popularity of Container as a Service (CaaS) clouds. The CaaS multi-tier applications could be optimized by using network topology, link or server load knowledge to choose the best endpoints to run in CaaS cloud. However, it is difficult to apply those optimizations to the public datacenter shared by multi-tenants. This is because of the opacity between the tenants and the datacenter providers: Providers have no insight into tenant's container workloads and dependencies, while tenants have no clue about the underlying network topology, link, and load. As a result, containers might be booted at wrong physical nodes that lead to performance degradation due to bi-section bandwidth bottleneck or co-located container interference. We propose 'DocMan', a toolset that adopts a black-box approach to discover container ensembles and collect information about intra-ensemble container interactions. It uses a combination of techniques such as distance identification and hierarchical clustering. The experimental results demonstrate that DocMan enables optimized containers placement to reduce the stress on bi-section bandwidth of the datacenter's network. The method can detect container ensembles at low cost and with 92% accuracy and significantly improve performance for multi-tier applications under the best of circumstances.

This work presents ChainFS, a middleware system that secures cloud storage services using a minimally trusted Blockchain. ChainFS hardens the cloud-storage security against forking attacks. The ChainFS middleware exposes a file-system interface to end users. Internally, ChainFS stores data files in the cloud and exports minimal and necessary functionalities to the Blockchain for key distribution and file operation logging. We implement the ChainFS system on Ethereum and S3FS and closely integrate it with FUSE clients and Amazon S3 cloud storage. We measure the system performance and demonstrate low overhead.

Web-based social networks enable new community-based opportunities for participants to engage, share their thoughts, and interact with each other. Theses related activities such as searching and advertising are threatened by spammers, content polluters, and malware disseminators. We propose a scalable spam detection system, termed Oases, for uncovering social spam in social networks using an online and scalable approach. The novelty of our design lies in two key components: (1) a decentralized DHT-based tree overlay deployment for harvesting and uncovering deceptive spam from social communities; and (2) a progressive aggregation tree for aggregating the properties of these spam posts for creating new spam classifiers to actively filter out new spam. We design and implement the prototype of Oases and discuss the design considerations of the proposed approach. Our large-scale experiments using real-world Twitter data demonstrate scalability, attractive load-balancing, and graceful efficiency in online spam detection for social networks.

In the age of big data, information sharing paradigm has been fundamentally changed: Data collection and consumption are increasingly decentralized (partly due to the advent of personal computing devices), and sharing personal data over the Internet becomes a prominent paradigm for new applications. One of these applications is Health Information Exchange (or HIE) where a patient's electronic medical record (EMR) was produced at one hospital, and is consumed by a physician in another hospital.

MOTIVATION: Genome-wide association studies (GWAS) have been widely used in discovering the association between genotypes and phenotypes. Human genome data contain valuable but highly sensitive information. Unprotected disclosure of such information might put individual's privacy at risk. It is important to protect human genome data. Exact logistic regression is a bias-reduction method based on a penalized likelihood to discover rare variants that are associated with disease susceptibility. We propose the HEALER framework to facilitate secure rare variants analysis with a small sample size. RESULTS: We target at the algorithm design aiming at reducing the computational and storage costs to learn a homomorphic exact logistic regression model (i.e. evaluate P-values of coefficients), where the circuit depth is proportional to the logarithmic scale of data size. We evaluate the algorithm performance using rare Kawasaki Disease datasets. AVAILABILITY AND IMPLEMENTATION: Download HEALER at http://research.ucsd-dbmi.org/HEALER/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The recent shift towards write-intensive workload on big data (e.g., financial trading, social user-generated data streams)has pushed the proliferation of log-structured key-value stores, represented by Google's BigTable [1], Apache HBase [2] andCassandra [3]. While providing key-based data access with aPut/Get interface, these key-value stores do not support value-based access methods, which significantly limits their applicability in modern web and database applications. In this paper, we present DELI, a DEferred Lightweight Indexing scheme on the log-structured key-value stores. To index intensively updated bigdata in real time, DELI aims at making the index maintenance as lightweight as possible. The key idea is to apply an append-only design for online index maintenance and to collect index garbage at carefully chosen time. DELI optimizes the performance of index garbage collection through tightly coupling its execution with a native routine process called compaction. The DELI's system design is fault-tolerant and generic (to most key-valuestores), we implemented a prototype of DELI based on HBase without internal code modification. Our experiments show that the DELI offers significant performance advantage for the write-intensive index maintenance.

In emerging information networks, it is crucially important to provide efficient search on distributed documents while preserving their owners' privacy, for which privacy preserving indexes or PPI presents a possible solution. An understudied problem for the PPI techniques is how to provide differentiated privacy preservation in the presence of multi-keyword document search. The differentiation is necessary as terms and phrases bear innate differences in their semantic meanings. In this paper, we present ϵ-MPPI, the first work to provide the distributed document search with quantitatively differentiated privacy preservation. In the design of ϵ-MPPI, we identified a suite of challenging problems and proposed novel solutions. For one, we formulated the quantitative privacy computation as an optimization problem that strikes a balance between privacy preservation and search efficiency. We also addressed the challenging problem of secure ϵ-MPPI construction in the multi-domain information network which lacks mutual trusts between domains. Towards a secure ϵ-MPPIconstruction with practically acceptable performance, we proposed to optimize the performance of secure multi-party computations by making a novel use of secret sharing. We implemented the ϵ-MPPI construction protocol with a functioning prototype. We conducted extensive experiments to evaluate the prototype's effectiveness and efficiency based on a real-world dataset.

Data outsourcing offers cost-effective computing power to manage massive data streams and reliable access to data. Data owners can forward their data to clouds, and the clouds provide data mirroring, backup, and online access services to end users. However, outsourcing data to untrusted clouds requires data authenticity and query integrity to remain in the control of the data owners and users.

Log-Structured-Merge (LSM) Tree gains much attention re-cently because of its superior performance in write-intensive workloads. LSM Tree uses an append-only structure in memory to achieve low write latency; at memory capac-ity, in-memory data are flushed to other storage media (e.g. disk). Consequently, read access is slower comparing to write. These specific features of LSM, including no in-place update and asymmetric read/write performance raise unique challenges in index maintenance for LSM. The structural difference between LSM and B-Tree also prevents mature B-Tree based approaches from being directly applied. To address the issues of index maintenance for LSM, we pro-pose Diff-Index to support a spectrum of index maintenance schemes to suit different objectives in index consistency and performance. The schemes consist of sync-full, sync-insert, async-simple and async-session. Experiments on our HBase implementation quantitatively demonstrate that Diff-Index offers various performance/consistency balance and satisfac-tory scalability while avoiding global coordination. Sync-insert and async-simple can reduce 60%-80 % of the overall index update latency when compared to the baseline sync-full; async-simple can achieve superior index update per-formance with an acceptable inconsistency. Diff-Index ex-ploits LSM features such as versioning and the flush-compact process to achieve goals of concurrency control and failure

In emerging information networks, having a privacy preserving index (or PPI) is critically important for locating information of interest for data sharing across autonomous providers while preserving privacy. An understudied problem for PPI techniques is how to provide controllable privacy preservation, given the innate difference of privacy concerns regarding different data owners. In this paper we present a personalized privacy preserving index, coined ε-PPI, which guarantees quantitative privacy preservation differentiated by personal identities. We devise a new common-identity attack that breaks existing PPI's and propose an identity-mixing protocol against the attack in ε-PPI. The proposed ε-PPI construction protocol is the first without any trusted third party and/or trust relationships between providers. We have implemented our ε-PPI construction protocol by using generic MPC techniques (secure multi-party computation) and optimized the performance to a practical level by minimizing the expensive MPC part.

In the age of big data, key-value data updated by intensive write streams is increasingly common, e.g., in social event streams. To serve such data in a cost-effective manner, a popular new paradigm is to outsource it to the cloud and store it in a scalable key-value store while serving a large user base. Due to the limited trust in third-party cloud infrastructures, data owners have to sign the data stream so that the data users can verify the authenticity of query results from the cloud. In this paper, we address the problem of verifiable freshness for multi-version key-value data. We propose a memory-resident digest structure that utilizes limited memory effectively and can have efficient verification performance. The proposed structure is named IncBM-Tree because it can INCrementally build a Bloom filter-embedded Merkle Tree. We have demonstrated the superior performance of verification under small memory footprints for signing, which is typical in an outsourcing scenario where data owners and users have limited resources.

Big data business can leverage and benefit from the Clouds, the most optimized, shared, automated, and virtualized computing infrastructures. One of the important challenges in processing big data in the Clouds is how to effectively partition the big data to ensure efficient distributed processing of the data. In this paper we present a Scalable and yet customizable data PArtitioning framework, called SPA, for distributed processing of big RDF graph data. We choose big RDF datasets as our focus of the investigation for two reasons. First, the Linking Open Data cloud has put forwards a good number of big RDF datasets with tens of billions of triples and hundreds of millions of links. Second, such huge RDF graphs can easily overwhelm any single server due to the limited memory and CPU capacity and exceed the processing capacity of many conventional data processing software systems. Our data partitioning framework has two unique features. First, we introduce a suite of vertexcentric data partitioning building blocks to allow efficient and yet customizable partitioning of large heterogeneous RDF graph data. By efficient, we mean that the SPA data partitions can support fast processing of big data of different sizes and complexity. By customizable, we mean that the SPA partitions are adaptive to different query types. Second, we propose a selection of scalable techniques to distribute the building block partitions across a cluster of compute nodes in a manner that minimizes inter-node communication cost by localizing most of the queries on distributed partitions. We evaluate our data partitioning framework and algorithms through extensive experiments using both benchmark and real datasets. Our experimental results show that the SPA data partitioning framework is not only efficient for partitioning and distributing big RDF datasets of diverse sizes and structures but also effective for processing big data queries of different types and complexity.

A known problem for virtualized cloud data centers is the inter-VM communication inefficiency for data transfer between co-resident VMs. Several engineering efforts have been made on building a shared memory based channel between co-resident VMs. The implementations differ in terms of whether user/program transparency, OS kernel transparency or VMM transparency is supported. However, none of existing works has engaged in an in-depth measurement study with quantitative and qualitative analysis on performance improvements as well as tradeoffs introduced by such a residency-aware inter-VM communication mechanism. In this paper we present an extensive experimental study, aiming at addressing a number of fundamental issues and providing deeper insights regarding the design of a shared memory channel for co-resident VMs. Example questions include how much performance gains can a residency-aware shared memory inter-VM communication mechanism provide under different mixtures of local and remote network I/O workloads, what overhead will the residence-awareness detection and communication channel switch introduce over the remote inter-VM communication, what factors may exert significant impact on the throughput and latency performance of such a shared memory channel. We believe that this measurement study not only helps system developers to gain valuable lessons and generate new ideas to further improve the inter-VM communication performance. It also offers new opportunities for cloud service providers to deploy their services more efficiently and for cloud service consumers to improve the performance of their application systems running in the Cloud.

Stream processing applications use online analytics to ingest high-rate data sources, process them on-the-fly, and generate live results in a timely manner. The data flow graph representation of these applications facilitates the specification of stream computing tasks with ease, and also lends itself to possible runtime exploitation of parallelization on multicore processors. While the data flow graphs naturally contain a rich set of parallelization opportunities, exploiting them is challenging due to the combinatorial number of possible configurations. Furthermore, the best configuration is dynamic in nature; it can differ across multiple runs of the application, and even during different phases of the same run. In this paper, we propose an autopipelining solution that can take advantage of multicore processors to improve throughput of streaming applications, in an effective and transparent way. The solution is effective in the sense that it provides good utilization of resources by dynamically finding and exploiting sources of pipeline parallelism in streaming applications. It is transparent in the sense that it does not require any hints from the application developers. As a part of our solution, we describe a light-weight runtime profiling scheme to learn resource usage of operators comprising the application, an optimization algorithm to locate best places in the data flow graph to explore additional parallelism, and an adaptive control scheme to find the right level of parallelism. We have implemented our solution in an industrial-strength stream processing system. Our experimental evaluation based on microbenchmarks, synthetic workloads, as well as real-world applications confirms that our design is effective in optimizing the throughput of stream processing applications without requiring any changes to the application code.

State monitoring is widely used for detecting critical events and abnormalities of distributed systems. As the scale of such systems grows and the degree of workload consolidation increases in Cloud data centers, node failures and performance interferences, especially transient ones, become the norm rather than the exception. Hence, distributed state monitoring tasks are often exposed to impaired communication caused by such dynamics on different nodes. Unfortunately, existing distributed state monitoring approaches are often designed under the assumption of always-online distributed monitoring nodes and reliable inter-node communication. As a result, these approaches often produce misleading results which in turn introduce various problems to Cloud users who rely on state monitoring results to perform automatic management tasks such as auto-scaling. This paper introduces a new state monitoring approach that tackles this challenge by exposing and handling communication dynamics such as message delay and loss in Cloud monitoring environments. Our approach delivers two distinct features. First, it quantitatively estimates the accuracy of monitoring results to capture uncertainties introduced by messaging dynamics. This feature helps users to distinguish trustworthy monitoring results from ones heavily deviated from the truth, yet significantly improves monitoring utility compared with simple techniques that invalidate all monitoring results generated with the presence of messaging dynamics. Second, our approach also adapts to non-transient messaging issues by reconfiguring distributed monitoring algorithms to minimize monitoring errors. Our experimental results show that, even under severe message loss and delay, our approach consistently improves monitoring accuracy, and when applied to Cloud application auto-scaling, outperforms existing state monitoring techniques in terms of the ability to correctly trigger dynamic provisioning.

Mix-zones are recognized as an alternative and complementary approach to spatial cloaking based approach to location privacy protection. Mix-zones break the continuity of location exposure by ensuring that users' movements cannot be traced while they reside in a mix-zone. In this paper we provide an overview of various known attacks that make mix-zones on road networks vulnerable and illustrate a set of counter measures to make road network mix-zones attack resilient. Concretely, we categorize the vulnerabilities of road network mix-zones into two classes: one due to the road network characteristics and user mobility, and the other due to the temporal, spatial and semantic correlations of location queries. For instance, the timing information of users' entry and exit into a mix-zone provides information to launch a timing attack. The non-uniformity in the transitions taken at the road intersection may lead to transition attack. An example query correlation attack is the basic continual query (CQ) attacks, which attempt to break the anonymity of road network aware mix-zones by performing query correlation based inference. The CQ-timing attacks carry out inference attacks based on both query correlation and timing correlation, and the CQ-transition attacks execute inference attacks based on both query correlation and transition correlation. We study the factors that impact on the effectiveness of each of these attacks and evaluate the efficiency of the counter measures, such as non-rectangle mix-zones and delay tolerant mix-zones, through extensive experiments on traces produced by GTMobiSim at different scales of geographic maps.

The past few years have witnessed an increasing demand for the next generation health information networks (e.g., NHIN[1]), which hold the promise of supporting large-scale information sharing across a network formed by autonomous healthcare providers. One fundamental capability of such information network is to support efficient, privacy-preserving (for both users and providers) search over the distributed, access controlled healthcare documents. In this paper we focus on addressing the privacy concerns of content providers; that is, the search should not reveal the specific association between contents and providers (a.k.a. content privacy). We propose SS-PPI, a novel privacy-preserving index abstraction, which, in conjunction of distributed access control-enforced search protocols, provides theoretically guaranteed protection of content privacy. Compared with existing proposals (e.g., flipping privacy-preserving index[2]), our solution highlights with a series of distinct features: (a) it incorporates access control policies in the privacy-preserving index, which improves both search efficiency and attack resilience; (b) it employs a fast index construction protocol via a novel use of the secrete-sharing scheme in a fully distributed manner (without trusted third party), requiring only constant (typically two) round of communication; (c) it provides information-theoretic security against colluding adversaries during index construction as well as query answering. We conduct both formal analysis and experimental evaluation of SS-PPI and show that it outperforms the state-of-the-art solutions in terms of both privacy protection and execution efficiency.

In this paper, we study the problem of indexing multidimensional data in P2P networks based on distributed hash tables (DHTs). We advocate the indexing approach that superimposes a multidimensional index tree on top of a DHT - a paradigm that keeps the underlying DHT intact while being able to adapt to any DHT substrate. In this context, we identify several index design issues and propose a novel indexing scheme called multidimensional Lightweight Hash Tree (m-LIGHT). First, to preserve data locality, m-LIGHT employs a clever naming mechanism that gracefully maps a tree-based index into the DHT and contributes to high efficiency in both index maintenance and query processing. Second, to tackle the load balancing issue, m-LIGHT leverages a new data-aware splitting strategy that achieves optimal load balance under a fixed index size. We present detailed algorithms for processing complex queries over the m-LIGHT index. We also conduct an extensive performance evaluation of m-LIGHT in comparison with several state-of-the-art indexing schemes. The experimental results show that m-LIGHT substantially reduces index maintenance overhead and improves query performance in terms of both bandwidth consumption and response latency.

DHT is a widely used building block for scalable P2P systems. However, as uniform hashing employed in DHTs destroys data locality, it is not a trivial task to support complex queries (e.g., range queries and k-nearest-neighbor queries) in DHT-based P2P systems. In order to support efficient processing of such complex queries, a popular solution is to build indexes on top of the DHT. Unfortunately, existing over-DHT indexing schemes suffer from either query inefficiency or high maintenance cost. In this paper, we propose LIGhtweight Hash Tree (LIGHT)—a query-efficient yet low-maintenance indexing scheme. LIGHT employs a novel naming mechanism and a tree summarization strategy for graceful distribution of its index structure. We show through analysis that it can support various complex queries with near-optimal performance. Extensive experimental results also demonstrate that, compared with state of the art over-DHT indexing schemes, LIGHT saves 50-75 percent of index maintenance cost and substantially improves query performance in terms of both response time and bandwidth consumption. In addition, LIGHT is designed over generic DHTs and hence can be easily implemented and deployed in any DHT-based P2P system.

In this paper, we study the problem of indexing multidimensional data in the P2P networks based on distributed hash tables (DHTs). We identify several design issues and propose a novel over-DHT indexing scheme called m- LIGHT. To preserve data locality, m-LIGHT employs a clever naming mechanism that gracefully maps the index tree into the underlying DHT so that it achieves efficient index maintenance and query processing. Moreover, m- LIGHT leverages a new data-aware index splitting strategy to achieve optimal load balance among peer nodes. We conduct an extensive performance evaluation for m-LIGHT. Compared to the state-of-the-art indexing schemes, m-LIGHT substantially saves the index maintenance overhead, achieves a more balanced load distribution, and improves the range query performance in both bandwidth consumption and response latency.

DHT is a widely-used building block in P2P systems, and complex queries are gaining popularity in P2P applications. To support efficient query processing over DHTs, effective indexing structures are essential. Recently, a number of indexing schemes have been proposed. However, these schemes have focused on improving query efficiency, and as a trade-off, sacrificed maintenance efficiency - an important performance measure in the P2P context, where frequent data updating and high peer dynamism are typically incurred. In this paper, we propose LHT, a Low maintenance Hash Tree, for efficient data indexing over DHTs. LHT employs a novel naming function and a tree summarization strategy to gracefully distribute its index structure. It is adaptable to any DHT substrates, and is easy to be implemented and deployed. Experiments show that in comparison with the state-of-the-art indexing technique, LHT saves up to 75% (at least 50%) maintenance cost, and achieves better performance for exact-match queries and range queries.