Organizational networks are vulnerable to trafficanalysis attacks that enable adversaries to infer sensitive information fromnetwork traffic—even if encryption is used. Typical anonymous communication networks are tailored to the Internet and are poorly suited for organizational networks.We present PriFi, an anonymous communication protocol for LANs, which protects users against eavesdroppers and provides high-performance traffic-analysis resistance. PriFi builds onDining Cryptographers networks (DC-nets), but reduces the high communication latency of prior designs via a new client/relay/server architecture, in which a client’s packets remain on their usual network path without additional hops, and in which a set of remote servers assist the anonymization process without adding latency. PriFi also solves the challenge of equivocation attacks, which are not addressed by related work, by encrypting traffic based on communication history. Our evaluation shows that PriFi introduces modest latency overhead (≈ 100ms for 100 clients) and is compatible with delay-sensitive applications such as Voice-over-IP.

Location check-ins contain both geographical and semantic information about the visited venues. Semantic information is usually represented by means of tags (e.g., “restaurant”). Such data can reveal some personal information about users beyond what they actually expect to disclose, hence their privacy is threatened. To mitigate such threats, several privacy protection techniques based on location generalization have been proposed. Although the privacy implications of such techniques have been extensively studied, the utility implications are mostly unknown. In this paper, we propose a predictive model for quantifying the effect of a privacy-preserving technique (i.e., generalization) on the perceived utility of check-ins. We first study the users' motivations behind their location check-ins, based on a study targeted at Foursquare users (N 1/4 77). We propose a machine-learning method for determining the motivation behind each check-in, and we design a motivation-based predictive model for the utility implications of generalization. Based on the survey data, our results show that the model accurately predicts the fine-grained motivation behind a check-in in [43%] of the cases and in [63%] of the cases for the coarse-grained motivation. It also predicts, with a mean error of [0.52] (on a scale from 1 to 5), the loss of utility caused by semantic and geographical generalization. This model makes it possible to design of utility-aware, privacy-enhancing mechanisms in location-based online social networks. It also enables service providers to implement locationsharing mechanisms that preserve both the utility and privacy for their users.

Permission systems are the main defense that mobile platforms, such as Android and iOS, offer to users to protect their private data from prying apps. However, due to the tension between usability and control, such systems have several limitations that often force users to overshare sensitive data. We address some of these limitations with SmarPer, an advanced permission mechanism for Android. To address the rigidity of current permission systems and their poor matching of users' privacy preferences, SmarPer relies on contextual information and machine learning methods to predict permission decisions at runtime. Note that the goal of SmarPer is to mimic the users' decisions, not to make privacy-preserving decisions per se. Using our SmarPer implementation, we collected 8,521 runtime permission decisions from 41 participants in real conditions. With this unique data set, we show that using an efficient Bayesian linear regression model results in a mean correct classification rate of 80% (±3%). This represents a mean relative reduction of approximately 50% in the number of incorrect decisions when compared with a user-defined static permission policy, i.e., the model used in current permission systems. SmarPer also focuses on the suboptimal trade-off between privacy and utility, instead of only "allow" or "deny" type of decisions, SmarPer also offers an "obfuscate" option where users can still obtain utility by revealing partial information to apps. We implemented obfuscation techniques in SmarPer for different data types and evaluated them during our data collection campaign. Our results show that 73% of the participants found obfuscation useful and it accounted for almost a third of the total number of decisions. In short, we are the first to show, using a large dataset of real in situ permission decisions, that it is possible to learn users' unique decision patterns at runtime using contextual information while supporting data obfuscation, this is an important step towards automating the management of permissions in smartphones.

Abstract In the past few years, we have witnessed a rise in the popularity of ride-hailing services (RHSs), an online marketplace that enables accredited drivers to use their own cars to drive ride-hailing users. Unlike other transportation services, RHSs raise significant privacy concerns, as providers are able to track the precise mobility patterns of millions of riders worldwide. We present the first survey and analysis of the privacy threats in RHSs. Our analysis exposes high-risk privacy threats that do not occur in conventional taxi services. Therefore, we propose PrivateRide, a privacy-enhancing and practical solution that offers anonymity and location privacy for riders, and protects drivers’ information from harvesting attacks. PrivateRide lowers the high-risk privacy threats in RHSs to a level that is at least as low as that of many taxi services. Using real data-sets from Uber and taxi rides, we show that PrivateRide significantly enhances riders’ privacy, while preserving tangible accuracy in ride matching and fare calculation, with only negligible effects on convenience. Moreover, by using our Android implementation for experimental evaluations, we show that PrivateRide’s overhead during ride setup is negligible. In short, we enable privacy-conscious riders to achieve levels of privacy that are not possible in current RHSs and even in some conventional taxi services, thereby offering a potential business differentiator.

Activity-tracking applications, where people record and upload information about their location-based activities (e.g., the routes of their activities), are increasingly popular. Such applications enable users to share information and compete with their friends on activity-based social networks but also, in some cases, to obtain discounts on their health insurance premiums by proving they conduct regular fitness activities. However, they raise privacy and security issues: the service providers know the exact locations of their users; the users can report fake location information, for example, to unduly brag about their performance. In this paper, we present SecureRun, a secure privacy-preserving system for reporting location-based activity summaries (e.g., the total distance covered and the elevation gain). SecureRun is based on a combination of cryptographic techniques and geometric algorithms, and it relies on existing Wi-Fi access-point networks deployed in urban areas. We evaluate SecureRun by using real data-sets from the FON hotspot community networks and from the Garmin Connect activity-based social network, and we show that it can achieve tight (up to a median accuracy of more than 80 percent) verifiable lower-bounds of the distance covered and of the elevation gain, while protecting the location privacy of the users with respect to both the social network operator and the access point network operator(s). The results of our online survey, targeted at RunKeeper users recruited through the Amazon Mechanical Turk platform, highlight the lack of awareness and significant concerns of the participants about the privacy and security issues of activity-tracking applications. They also show a good level of satisfaction regarding SecureRun and its performance.

Millions of users rely on email providers to manage and store their personal communications. This vast amount of private information, however, is often misused not only by adversaries, but also by the providers themselves. End-to-end email encryption is considered the most robust defense against this threat, however, its many requirements make this approach impractical for protecting everyday emails. In this paper, we present Email Cloak, an email alias service with public key encryption capabilities. Email Cloak relaxes email encryption requirements by relying on a privacy-respecting third-party. Emails sent and received by the user are automatically encrypted with her public key by Email Cloak before being forwarded to, and stored by her email provider. This approach, while seemingly straightforward, offers multiple benefits: simplified key management, selective and automatic encryption, advanced deployment options and transparency towards other parties. Moreover, our experimental evaluation shows that the overhead introduced by Email Cloak is adequate for email communications. We have also made our implementation publicly available. In doing so, we deliver a practical and flexible tool that provides privacy-concerned users with greater control over their stored emails.

Mobile Shopping Applications (MSAs) are rapidly gaining popularity. They enhance the shopping experience, by offering customized recommendations or incorporating customer loyalty programs. Although MSAs are quite effective at attracting new customers and binding existing ones to a retailer's services, existing MSAs have several shortcomings. The data collection practices involved in MSAs and the lack of transparency thereof are important concerns for many customers. This paper presents inShopnito, a privacy-preserving mobile shopping application. All transactions made in inShopnito are unlinkable and anonymous. However, the system still offers the expected features from a modern MSA. Customers can take part in loyalty programs and earn or spend loyalty points and electronic vouchers. Furthermore, the MSA can suggest personalized recommendations even though the retailer cannot construct rich customer profiles. These profiles are managed on the smartphone and can be partially disclosed in order to get better, customized recommendations. Finally, we present an implementation called inShopnito, of which the security and performance is analyzed. In doing so, we show that it is possible to have a privacy-preserving MSA without having to sacrifice practicality.

Mobile applications increasingly require users to surrender private information, such as GPS location or social networking data. To facilitate user privacy when using these applications, secure function evaluation SFE could be used to obliviously compute functions over encrypted inputs. The dominant construction for desktop applications is the Yao garbled circuit, but this technique requires significant processing power and network overhead, making it extremely expensive on resource-constrained mobile devices. In this work, we develop Efficient Mobile Oblivious Computation, a set of SFE protocols customized for the mobile platform. Using partially homomorphic cryptosystems, we develop protocols to meet the needs of two popular application types: location-based and social networking. Using these applications as comparison benchmarks, we demonstrate execution time improvements of 99% and network overhead improvements of 96% over the most optimized garbled circuit techniques. These results show that our protocols provide mobile application developers with a more practical and equally secure alternative to garbled circuits. Copyright © 2013 John Wiley & Sons, Ltd.

The security guarantees provided by SSL/TLS depend on the correct authentication of servers through certificates signed by a trusted authority. However, as recent incidents have demonstrated, trust in these authorities is not well placed. Increasingly, certificate authorities (by coercion or compromise) have been creating forged certificates for a range of adversaries, allowing seemingly secure communications to be intercepted via man-in-the-middle (MITM) attacks. A variety of solutions have been proposed, but their complexity and deployment costs have hindered their adoption. In this paper, we propose Direct Validation of Certificates (DVCert), a novel protocol that, instead of relying on third-parties for certificate validation, allows domains to directly and securely vouch for their certificates using previously established user authentication credentials. By relying on a robust cryptographic construction, this relatively simple means of enhancing server identity validation is not only efficient and comparatively easy to deploy, but it also solves other limitations of third-party solutions. Our extensive experimental analysis in both desktop and mobile platforms shows that DVCert transactions require little computation time on the server (e.g., less than 1 ms) and are unlikely to degrade server performance or user experience. In short, we provide a robust and practical mechanism to enhance server authentication and protect web applications from MITM attacks against SSL/TLS. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

HTTP cookies are the de facto mechanism for session authentication in Web applications. However, their inherent security weaknesses allow attacks against the integrity of Web sessions. HTTPS is often recommended to protect cookies, but deploying full HTTPS support can be challenging due to performance and financial concerns, especially for highly distributed applications. Moreover, cookies can be exposed in a variety of ways even when HTTPS is enabled. In this article, we propose one-time cookies (OTC), a more robust alternative for session authentication. OTC prevents attacks such as session hijacking by signing each user request with a session secret securely stored in the browser. Unlike other proposed solutions, OTC does not require expensive state synchronization in the Web application, making it easily deployable in highly distributed systems. We implemented OTC as a plug-in for the popular WordPress platform and as an extension for Firefox and Firefox for mobile browsers. Our extensive experimental analysis shows that OTC introduces a latency of less than 6 ms when compared to cookies—a negligible overhead for most Web applications. Moreover, we show that OTC can be combined with HTTPS to effectively add another layer of security to Web applications. In so doing, we demonstrate that one-time cookies can significantly improve the security of Web applications with minimal impact on performance and scalability.

Anonymous communications systems generally trade off performance for strong cryptographic guarantees of privacy. However, a number of applications with moderate performance requirements (e.g., chat) may require both properties. In this paper, we develop a new architecture that provides provably unlinkable and efficient communications using a single intermediary node. Nodes participating in these Mix-In-Place Networks (MIPNets) exchange messages through a mailbox in an Oblivious Proxy (OP). Clients leverage Secure Function Evaluation (SFE) to send and receive their messages from the OP while blindly but reversibly modifying the appearance of all other messages (i.e., mixing in place) in the mailbox. While an Oblivious Proxy will know that a client participated in exchanges, it can not be certain which, if any, messages that client transmitted or received. We implement and measure our proposed design using a modified version of Fairplay and note reductions in execution times of greater than 98% over the naïve application of garbled circuits. We then develop a chat application on top of the MIPNet architecture and demonstrate its practical use for as many as 100 concurrent users. Our results demonstrate the potential to use SFE-enabled "mixing" in a single proxy as a means of providing provable deniability for applications with near real-time performance requirements.

The performance of SIP proxies is critical for the robust operation of many applications. However, the use of even light-weight authentication schemes can significantly degrade throughput in these systems. In particular, systems in which multiple proxies share a remote authentication database can experience reduced performance due to latency. In this paper, we investigate how the application of parallel execution and batching can be used to maximize throughput while carefully balancing demands for bandwidth and call failure rates. Through the use of a modified version of OpenSER, a high-performance SIP proxy, we demonstrate that the traditional recommendation of simply launching a large number of parallel processes not only incurs substantial overhead and increases dropped calls, but can actually decrease call throughput. An alternative technique that we implement, request batching, similarly fails to achieve high proxy throughput. Through a carefully selected mix of batching and parallelization, we reduce the bandwidth required to maximize authenticated signaling throughput by the proxy by more than 75 percent. This mix also keeps the call loss rates below 1 percent at peak performance. In addition, we demonstrate that the delay introduced by batching is acceptable for VoIP applications. As a result, our technique significantly reduce the cost and increase the throughput of authentication for large-scale networks supporting SIP applications.

A growing class of applications, including VoIP, IM and presence, are enabled by the session initiation protocol (SIP). Requests in SIP typically traverse through multiple proxies. The availability of multiple proxies offers the flexibility to distribute proxy functionality across several nodes. In particular, after experimentally demonstrating that the resource consumption of maintaining state is significant, we define the problem of state distribution across multiple nodes when the goal is to increase overall call throughput. We first formulate this as an optimization problem and then derive a distributed algorithm from it. This distributed algorithm leads to the design and evaluation of SERvartuka, a more scalable SIP server that dynamically determines the number of SIP requests for which the server is stateful while delegating state maintenance for the remainder of the requests to a server further downstream. This design is in contrast to existing SIP servers that are statically configured to either be stateless or stateful and therefore result in sub-optimal call throughput. We implement SERvartuka on top of OpenSER, a commercial SIP proxy server and measure performance benefits of different server configurations. An example of our results is a 20% percent increase in call throughput when using our algorithm for a configuration of two servers in series.