Sharing photos through Online Social Networks is an increasingly popular fashion. However, it poses a seriousthreat to end users as private information in the photos maybe inappropriately shared with others without their consent. This paper proposes a design and implementation of a system using a dynamic privacy preserving partial image sharing technique (namely PUPPIES), which allows data owners to stipulate specific private regions (e.g., face, SSN number) in an image and correspondingly set different privacy policies for each user. As a generic technique and system, PUPPIES targets at threats about over-privileged and unauthorized sharing of photos at photo service provider (e.g., Flicker, Facebook, etc) side. To this end, PUPPIES leverages the image perturbation technique to "encrypt" the sensitive areas in the original images, and therefore it can naturally support popular image transformations (such as cropping, rotation) and is well compatible with most image processing libraries. The extensive experiments on 19,000 images demonstrate that PUPPIES is very effective for privacy protection and incurs only a small computational overhead. In addition, PUPPIES offers high flexibility for different privacy settings, and is very robust to different types of privacy attacks.

Due to the lossy nature of wireless links, it is difficult to determine if packet losses are due to wireless-induced effects or from malicious discarding. Many prior efforts on detecting malicious packet drops rely on evidence collected via passive monitoring by neighbor nodes. However, they do not analyze the cause of packet losses. In this paper, we ask: 1) Given certain macroscopic parameters of the network (like traffic intensity and node density) what is the likelihood that evidence exists with respect to a transmission? 2) How can these parameters be used to perform a forensic analysis of the reason for the losses? Toward answering the above questions, we first build an analytical framework that computes the likelihood that evidence (we call this transmission evidence, or TE for short) exists with respect to transmissions, in terms of a set of network parameters. We validate our analytical framework via both simulations as well as real-world experiments on two different wireless testbeds. The analytical framework is then used as a basis for a protocol within a forensic analyzer to assess the cause of packet losses and determine the likelihood of forwarding misbehaviors. Through simulations, we find that our assessments are close to the ground truth in all examined cases, with an average deviation of 2.3% from the ground truth and a worst case deviation of 15.0%.

Web map services today, such as Google and Bing maps, have digitalized a great portion of the physical world into easily accessible location databases. After the industry invested huge efforts in gathering related information, a user now can search a physical location on the map and know what kind of place it is, known as reverse geo-coding. However, this functionality is mostly limited to public outdoor locations and to building level granularity. We believe that many services can benefit from knowing the semantic meanings of fine-grained locations including indoor places. For example, the phone can mute and delay incoming calls when a user enters a meeting room. Cameras can be disabled in bathrooms to protect users' privacy. In this paper, we present PinPlace, an on-device service that can automatically associate semantic meanings with outdoor and indoor locations using the activity, transit, and time related features.

Sharing photos through Online Social Networks becomes an increasingly popular fashion. However, users' privacy may be at stake when sensitive photos are shared improperly. This paper presents a dynamic privacy protection technique (named PuPPIeS) for image data where the data owner stipulates small private regions for sensitive objects (faces, SSN numbers, etc.) of a photo/image and sets different sharing policies for these partial regions with respect to different individuals. PuPPIeS is based on optimized reversible matrix perturbation of compressed image data. Hence it can naturally support frequently used image transformations. Our experiments show that our solution is effective for privacy protection and incurs only a small overhead for partial image sharing.

Through a controlled online experiment with 447 Android phone users using their own devices, we investigated how empowering users with information-disclosure control and enhancing their ads awareness affect their installation behaviors, information disclosure, and privacy perceptions toward different mobile apps. In the 3 (control: no, low, high) x 2 (ads awareness: absent, present) x 3 (app context: Wallpaper, BusTracker, Flashlight) fractional factorial between-subjects experiment, we designed privacy notice dialogs that simulate real Android app pre-installation privacy-setting interfaces to implement and manipulate control and ads awareness. Our findings suggest that empowering users with control over information disclosure and enhancing their ads awareness before installation effectively help them make better privacy decisions, increase their likelihood of installing an app, and improve their perceptions of the app. Implications for designing mobile apps' privacy notice dialogs and potential separate-ads-control solutions are discussed.

The proliferation of mobile apps is due in part to the advertising ecosystem which enables developers to earn revenue while providing free apps. Ad-supported apps can be developed rapidly with the availability of ad libraries. However, today?s ad libraries essentially have access to the same resources as the parent app, and this has caused signi?cant privacy concerns. In this paper, we explore ef?cient methods to de-escalate privileges for ad libraries where the resource access privileges for ad libraries can be different from that of the app logic. Our system, PEDAL, contains a novel machine classi?er for detecting ad libraries even in the presence of obfuscated code, and techniques for automatically instrumenting bytecode to effect privilege de-escalation even in the presence of privilege inheritance. We evaluate PEDAL on a large set of apps from the Google Play store and demonstrate that it has a 98% accuracy in detecting ad libraries and imposes less than 1% runtime overhead on apps.

Mobile app ecosystems have experienced tremendous growth in the last six years. This has triggered research on dynamic analysis of performance, security, and correctness properties of the mobile apps in the ecosystem. Exploration of app execution using automated UI actions has emerged as an important tool for this research. However, existing research has largely developed analysis-specific UI automation techniques, wherein the logic for exploring app execution is intertwined with the logic for analyzing app properties. PUMA is a programmable framework that separates these two concerns. It contains a generic UI automation capability (often called a Monkey) that exposes high-level events for which users can define handlers. These handlers can flexibly direct the Monkey's exploration, and also specify app instrumentation for collecting dynamic state information or for triggering changes in the environment during app execution. Targeted towards operators of app marketplaces, PUMA incorporates mechanisms for scaling dynamic analysis to thousands of apps. We demonstrate the capabilities of PUMA by analyzing seven distinct performance, security, and correctness properties for 3,600 apps downloaded from the Google Play store.

In social swarming applications, participants equipped with 3G and WiFi-capable smartphones are tasked to provide reports (possibly voluminous ones that include full-motion video) about their immediate environment to a central coordinator. In this paper, we consider the problem of timely delivery of these reports: Each report has an associated deadline, and the goal of the system is to retrieve as many reports as possible (or retrieve the most valuable reports), while satisfying each report's deadline. Reporters can use their cellular interface to upload their reports but can also ask neighbors (using their faster WiFi interface) to help upload parts of their reports. Under an assumption that WiFi transmission delays are negligible, we first show that there exists a polynomial time optimal solution using an earliest-deadline-first (EDF) strategy for achieving the goals described above. In practice, WiFi delays are not negligible; in this case, it turns out that the scheduling problem is strongly NP-hard. We formulate two heuristic algorithms, and show, through simulations and experiments on an Android-based implementation, that these heuristics perform 2-4× better than without peer-assistance, and within 60% of an upper-bound on the optimal.

Process variations are growing with technology scaling towards nano-scale. This brings new challenges to the design of memory modules, which are often the first circuits to be fabricated using a new technology and usually designed with critical timing. We observed that several delay faults, which are dependent on address transitions, may escape traditional march tests. This paper presents a new march test WT that targets such delay faults. Through Monte Carlo simulations and analytical studies on SRAM designs using an industrial 65nm process, we have demonstrated that WT provides a faulty-chip-coverage that is close to 100%. Most importantly, this is the first march test with test length that targets address-dependent delay faults and hence the first delay test which can be used in practice.

As process variations grow with technology scaling, failure rates increase and are predicted to be so high as to render devices unusable for computing domain. In order to continue to benefit from scaling, three dimensions can be explored: increasing operational margins, testing, and the resilience of applications. The solutions in each dimension bring out different trade-offs in yield, failure rate, test cost, and performance. We use a ternary content-addressable memory (TCAM) as a case study to better understand these trade-offs. We develop two new delay tests for TCAMs, and a new method to estimate yield, failure rate, test cost, and performance of TCAM under process variations when various tests are used. Our results show that with little test overhead and negligible yield loss, our new tests can significantly decrease the failure rates of TCAMs shipped to customers.

To address the pressing need to provide transparency into the online targeted advertising ecosystem, we present AdReveal, a practical measurement and analysis framework, that provides a first look at the prevalence of different ad targeting mechanisms. We design and implement a browser based tool that provides detailed measurements of online display ads, and develop analysis techniques to characterize the contextual, behavioral and re-marketing based targeting mechanisms used by advertisers. Our analysis is based on a large dataset consisting of measurements from 103K webpages and 139K display ads. Our results show that advertisers frequently target users based on their online interests; almost half of the ad categories employ behavioral targeting. Ads related to Insurance, Real Estate and Travel and Tourism make extensive use of behavioral targeting. Furthermore, up to 65% of ad categories received by users are behaviorally targeted. Finally, our analysis of re-marketing shows that it is adopted by a wide range of websites and the most commonly targeted re-marketing based ads are from the Travel and Tourism and Shopping categories.

In social swarming applications, participants equipped with 3G and WiFi-capable smart phones are tasked to provide reports (possibly voluminous ones that include full-motion video) about their immediate environment to a central coordinator. In this paper, we consider the problem of timely delivery of these reports: each report has an associated deadline and the goal of the system is to retrieve as many reports as possible (or retrieve the most valuable reports), while satisfying each report's deadline. Reporters can use their cellular interface to upload their reports, but can also ask neighbors (using their faster WiFi interface) to help upload parts of their reports. Under an assumption that WiFi transmission delays are negligible, we first show that there exists a polynomial time optimal solution using an earliest-deadline-first (EDF) strategy for achieving the goals described above. In practice, WiFi delays are not negligible: in this case, it turns out that the scheduling problem is strongly NP-hard. We formulate two heuristic algorithms, and show, through simulations with real-world measurements, that these heuristics perform 2-4× better than without peer-assistance, and within 60% of an upper-bound on the optimal.

Networked systems have benefited from unprecedented growth in hardware capabilities, but, as we move closer to the end of the Moore's law era, future networked systems are likely to be more constrained by hardware capabilities than they have been in the past. We take the position that the networking community should, in response to this development, proactively and systematically develop networking roadmaps, which attempt to predict how trends in hardware capabilities will impact networked systems. In this paper, we discuss a possible methodology for developing networking roadmaps, and present two case studies that illustrate the methodology and reveal how increasing hardware unreliability can affect the performance of routing and transport protocols.

Due to the lossy nature of wireless links, it is difficult to determine if packet losses are due to wireless-induced effects or from malicious discarding. Many prior efforts on detecting malicious packet drops rely on evidence collected via passive monitoring by neighbor nodes; however, they do not analyze the cause of packet losses. In this paper, we ask: (a) Given certain macroscopic parameters of the network (like traffic intensity and node density) what is the likelihood that evidence exists with respect to a transmission? and, (b) How can these parameters be used to perform a forensic analysis of the reason for the losses? Towards answering the above questions, we first build an analytical framework that computes the likelihood that evidence (we call this transmission evidence or TE for short) exists with respect to transmissions, in terms of a set of network parameters. We validate our analytical framework via both simulations as well as real-world experiments on two different wireless testbeds. The analytical framework is then used as a basis for a protocol within a forensic analyzer to assess the cause of packet losses and determine the likelihood of forwarding misbehaviors. Through simulations, we find that our assessments are close to the ground truth in all examined cases, with an average deviation of 2.3% from the ground truth and a worst case deviation of 15.0%.

The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.

The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.

In this paper, we consider the design of a system in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured. Thus, in this paper, we focus on privacy-preserving collaborative learning for the mobile setting, which addresses several competing challenges not previously considered in the literature: supporting complex classification methods like support vector machines, respecting mobile computing and communication constraints, and enabling user-determined privacy levels. Our approach, Pickle, ensures classification accuracy even in the presence of significantly perturbed training samples, is robust to methods that attempt to infer the original data or poison the model, and imposes minimal costs. We validate these claims using a user study, many real-world datasets and two different implementations of Pickle.

With the advent of smartphone technology, it has become possible to conceive of entirely new classes of applications. Social swarming, in which users armed with smartphones are directed by a central director to report on events in the physical world, has several real-world applications: search and rescue, coordinated fire-fighting, and the DARPA balloon hunt challenge. In this paper, we focus on the following problem: how does the director optimize the selection of reporters to deliver credible corroborating information about an event. We first propose a model, based on common notions of believability, about the credibility of information. We then cast the problem posed above as a discrete optimization problem, prove hardness results, introduce optimal centralized solutions, and design an approximate solution amenable to decentralized implementation whose performance is about 20 percent off, on average, from the optimal (on real-world data sets derived from Google News) while being three orders of magnitude more computationally efficient. More interesting, a time-averaged version of the problem is amenable to a novel stochastic utility optimization formulation, and can be solved optimally, while in some cases yielding decentralized solutions. To our knowledge, we are the first to propose and explore the problem of extracting credible information from a network of smartphones.

With the advent of smartphone technology, it has become possible to conceive of entirely new classes of applications. Social swarming, in which users armed with smartphones are directed by a central director to report on events in the physical world, has several real-world applications. In this paper, we focus on the following problem: how does the director optimize the selection of reporters to deliver credible corroborating information about an event? We first propose a model, based on common intuitions of believability, about the credibility of information. We then cast the problem as a discrete optimization problem, and introduce optimal centralized solutions and an approximate solution amenable to decentralized implementation whose performance is about 20% off on average from the optimal while being 3 orders of magnitude more computationally efficient. More interesting, a time-averaged version of the problem is amenable to a novel stochastic utility optimization formulation, and can be solved optimally, while in some cases yielding decentralized solutions.

Energy efficient communication is a key requirement of energy-constrained underwater sensor networks (UWSNs). In this paper, we show that the cooperative diversity, which is conventionally utilized to improve reliability in UWSNs, can be employed to reduce energy consumption and preserve a reasonable level of data reliability and communication delay. We first elucidate in what circumstances the cooperative diversity saves energy compared to the non-cooperative diversity. We show that this largely depends on parameters such as distance between the source and the destination node, the location of potential partner node, and the requirements of reliability and communication delay. Second, we propose a simple but effective cooperation scheme to take advantage of the cooperative diversity. The cooperation diversity can achieve a near-optimal energy saving in some circumstances, and it is no worse than the non-cooperative diversity in all cases. Our work provides instructive theoretical guidelines for designing practical UWSNs.

Underwater sensor networks (UWSNs) have been proposed for many location-dependent applications such as oceanographic data collection, pollution monitoring, mine reconnaissance, etc. Accurate node localization plays an important role in realizing the potential gains of these applications. Although many localization algorithms have been proposed for terrestrial sensor networks in recent years, it is not feasible to directly use these algorithms in UWSNs since UWSNs lack a fast and reliable communication channel. Further, due to their slow convergence speeds and high communication overhead, distributed localization algorithms designed for small-scale UWSNs are not practical for large-scale underwater sensor systems. To achieve accurate and energy efficient node localization in large-scale UWSNs, an asymmetrical round trip based localization (ARTL) algorithm is proposed in this paper. This algorithm has low computational complexity and excellent scalability. Without time synchronization, this algorithm can achieve highly accurate ranging in large-scale UWSNs. Simulation results demonstrate the effectiveness of our design in terms of both localization accuracy and energy consumption.

Underwater communication is a challenging topic due to its singular channel characteristics. Most protocols used in terrestrial wireless communication can not be directly applied in the underwater world. In this paper, we focus on the issue of energy efficient transmission in underwater sensor networks (UWSNs) and analyze this problem in a rigorous and theoretical way. We formalize an optimization problem which aims to minimize energy consumption and simultaneously accounts for other performance metrics such as the data reliability and the communication delay. With the help of Karush-Kuhn-Tucker conditions (KKT conditions), we derive a simple and explicit, but nevertheless accurate, approximate solution under reasonable assumptions. This approximate solution provides theoretical guidelines for designing durable and reliable UWSNs. Our result also shows that reliability and communication delay are crucial factors to the energy consumption for transmission.

Multiple transmission rates are supported in the current 802.11 WLANs, which allows stations to exploit different rates in an adaptive manner to cope with the variability of wireless channels and achieve the higher system throughput. Many efforts have been made at rate adaptation in the literature. The design of rate adaptation algorithms needs to take four basic issues into account. In this paper, by properly addressing the four issues, we present a novel rate adaptation algorithm, idle detection based optimal throughput rate adaptation (ITRA). We apply a comprehensive approach to designing the rate adaptation mechanism and attempt to maximize the overall system throughput. ITRA is furnished with two instructive features. First, ITRA modifies the conventional exponential backoff rule to a constant one. With the help of constant contention window (CW), ITRA can discriminate collisions from channel errors, and does not need RTS/CTS handshakes which are adopted by many existing works. We derive an explicit relation between the throughput and the CW size, and choose the CW size to optimize the throughput as well as improve the fairness. Secondly, ITRA directly estimates the current network throughput and changes transmission rate according to the estimation. Since the fundamental purpose of rate adaptation is to improve the network throughput, using the throughput as the metric is more straightforward than applying indirect criteria like frame transmission statistics or PHY metrics. We evaluate our proposed scheme by simulation, and the result shows that ITRA achieves satisfactory performance.

The mobility of underwater sensor nodes makes the network topology inconveniently controlled and slowly changed. Thus, in order to enable underwater sensor networks to work more effectively, it is necessary for us to periodically detect the coverage rate and redeploy nodes to non-coverage areas. In this paper, we take the lead in introducing the redeployment issue in underwater sensor networks. In our opinion, the key point of the redeployment issue is coverage. For this special coverage topic, we first propose a coverage rate definition scheme. Then along with the definitions, two redeployment algorithms are introduced, of which one is based on adding new nodes while the other one is by the means of moving redundant ones. By modeling the mobility behavior of underwater nodes with three-dimensional random walks, we employ simulation experiments to verify our ideas, the results of which show the importance of redeployment in the underwater environment.

As the most important issue in wireless sensor networks, power saving is catching researchers' great attentions all the time. Among several power saving strategies, one of the best methods is to make unused components inactive whenever possible, i.e. applying sleep scheduling schemes to sensors. In this paper, by analyzing and concluding existing works, we propose four sleep scheduling schemes (ACAA, SRAA, SAA and NCAA) for different kinds of application environments, then analyze each of them by stochastic petri net (SPN). We can easily get the average power consumptions and event delays of sensor nodes by using the steady state probability matrix of the SPN models. Moreover, the numeric results show that this concise graphic analysis method is suitable for analyzing sleep scheduling schemes.

In this paper, by establishing the probability models, we systematically and comprehensively analyze the roles of the packet retransmission, the block retransmission, and the erasure coding in the reliable transport of wireless sensor networks. And as well as the three kinds of packet level schemes, we also consider the effect of two kinds of bit level strategies, CRC and FEC. At last, based on the numeric results, the appropriate schemes for different BER (high, medium, and low) are determined, and we also present some principles that reveal profound insights in designing reliable protocols and mechanisms in wireless sensor networks.

Underwater communications is a very challenging topic due to its singular channel characteristics. Most protocols used in terrestrial wireless communications can not be directly applied in the underwater world. A high bit error rate and low propagation delay make the design of reliable transport protocols especially awkward. In this paper, we first propose four schemes that combine forward error correction mechanisms at the bit and/or packet level to increase the reliability in a non-cooperative scenario. The broadcast property of the underwater environment allows us to extend them to a cooperative setting. Based on our analyses, we introduce ADELIN: an adaptive reliable transport protocol for underwater sensor networks. We suggest an architecture for implementation and compare our protocol to other schemes. We show that it succeeds in a better probability and energy tradeoff for both single- and multi-hop communications.

In underwater sensor networks (UWSNs), high-delay acoustic channels are used for communications, which introduces new challenges for analyzing and choosing proper reliable transport mechanisms. In this paper, we investigate four key parameters (node distance, communication frequency, packet length and SNR) influencing the transport of UWSNs. Since treating all the four factors equally will make the analysis almost intractable, we propose a four-step analytical framework. In this framework, by optimizing both the communication frequency and packet length, we firstly show that the average energy consumption and the average communication delay of different transport schemes are only related to node distance and SNR. Then when considering practical scenarios, we further demonstrate that the scheme performances are mainly determined by SNR, which rather simplifies the analysis process. In addition, an important metric PEDP is proposed, which is only related to SNR but has the capability of evaluating the performance of transport schemes roundly and effectively. Finally, the numeric results offer us some profound insights for choosing proper transport schemes in UWSNs.