Context-Dependent Privacy-Aware Photo Sharing based on Machine Learning

Photo privacy has raised a growing concern with the advancements of image analytics, face recognition, and deep learning techniques widely applied on social media. If properly deployed, these powerful techniques can in turn assist people in enhancing their online privacy. One possible approach is to build a strong, automatic and dynamic access control mechanism based on analyzing the image content and learning users sharing behavior. This paper presents a model for context-dependent and privacy-aware photo sharing based on machine learning. The proposed model utilizes image semantics and requester contextual information to decide whether or not to share a particular picture with a specific requester at certain context, and if yes, at which granularity. To evaluate the proposed model, we conducted a user study on 23 subjects and collected a dataset containing 1’018 manually annotated images with 12’216 personalized contextual sharing decisions. Evaluation experiments were performed and the results show a promising performance of the proposed model for photo sharing decision making. Furthermore, the influences of different types of features on decision making have been investigated, the results of which validate the usefulness of pre-defined features and imply a significant variance between users sharing behaviors and privacy attitudes

A Predictive Model for User Motivation and Utility Implications of Privacy-Protection Mechanisms in Location Check-Ins

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 = 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 location-sharing mechanisms that preserve both the utility and privacy for their users

Controlled Data Sharing for Collaborative Predictive Blacklisting

Although sharing data across organizations is often advocated as a promising way to enhance cybersecurity, collaborative initiatives are rarely put into practice owing to confidentiality, trust, and liability challenges. In this paper, we investigate whether collaborative threat mitigation can be realized via a controlled data sharing approach, whereby organizations make informed decisions as to whether or not, and how much, to share. Using appropriate cryptographic tools, entities can estimate the benefits of collaboration and agree on what to share in a privacy-preserving way, without having to disclose their datasets. We focus on collaborative predictive blacklisting, i.e., forecasting attack sources based on one's logs and those contributed by other organizations. We study the impact of different sharing strategies by experimenting on a real-world dataset of two billion suspicious IP addresses collected from Dshield over two months. We find that controlled data sharing yields up to 105% accuracy improvement on average, while also reducing the false positive rate.Comment: A preliminary version of this paper appears in DIMVA 2015. This is the full version. arXiv admin note: substantial text overlap with arXiv:1403.212

Privacy-Preserving Optimal Meeting Location Determination on Mobile Devices

Equipped with state-of-the-art smartphones and mobile devices, today's highly interconnected urban population is increasingly dependent on these gadgets to organize and plan their daily lives. These applications often rely on current (or preferred) locations of individual users or a group of users to provide the desired service, which jeopardizes their privacy; users do not necessarily want to reveal their current (or preferred) locations to the service provider or to other, possibly untrusted, users. In this paper, we propose privacy-preserving algorithms for determining an optimal meeting location for a group of users. We perform a thorough privacy evaluation by formally quantifying privacy-loss of the proposed approaches. In order to study the performance of our algorithms in a real deployment, we implement and test their execution efficiency on Nokia smartphones. By means of a targeted user-study, we attempt to get an insight into the privacy-awareness of users in location-based services and the usability of the proposed solutions

The Effect of Pre-Veraison Smoke Exposure of Grapes on Phenolic Compounds and Smoky Flavour in Wine

Background and Aims. Smoke exposure occurred in the Adelaide Hills region in December 2019 due to a wildfire, when wine grapes were peppercorn-size green berries. Previously, pre-veraison smoke exposure had been identified through model experiments as unlikely to affect grape composition, whereas smoke exposure after veraison can have a major effect on wine flavour. Hence the effects of pre-veraison smoke on grape and wine composition, and smoky sensory properties of wine were investigated. Methods and Results. Chardonnay, Pinot Noir and Shiraz were investigated and eight blocks with varied smoke exposure were selected for each cultivar. Berries were sampled initially four weeks after the fire and at harvest, and mature grapes were made into unoaked wines. Established smoke exposure markers, phenolic glycosides, were found in berries at pre-veraison and at harvest from the high smoke exposure sites, with concentrations well above those found in non-smoke exposed fruit. Volatile phenols were also elevated in grapes at harvest. The resulting red wines from some exposure vineyards were high in volatile phenols, glycosides and smoky flavours. However, most of the Chardonnay wines expressed much less smoky flavours, despite similar levels of smoke exposure of grapes. Conclusions. Pre-veraison smoke exposure can result in elevated concentrations of volatile phenols and their glycosidic metabolites in grape berries and wine and cause strong smoky flavour in wine. Significance. The wine sector and land management agencies responsible for controlled burns need to consider the effect of smoke from fires near vineyards even very early in the growing season

k-indistinguishable traffic padding in web applications

Abstract. While web-based applications are becoming increasingly ubiquitous, they also present new security and privacy challenges. In particular, recent research revealed that many high profile Web applications might cause private user information to leak from encrypted traffic due to side-channel attacks exploiting packet sizes and timing. Moreover, existing solutions, such as random padding and packet-size rounding, are shown to incur prohibitive cost while still not ensuring sufficient privacy protection. In this paper, we propose a novel k-indistinguishable traffic padding technique to achieve the optimal tradeoff between privacy protection and communication and computational cost. Specifically, we first present a formal model of the privacy-preserving traffic padding (PPTP). We then formulate PPTP problems under different application scenarios, analyze their complexity, and design efficient heuristic algorithms. Finally, we confirm the effectiveness and efficiency of our algorithms by comparing them to existing solutions through experiments using real-world Web applications.

Modelling Smoke Flavour in Wine from Chemical Composition of Smoke-Exposed Grapes and Wine

Wine grapes exposed to smoke and wine made from grapes exposed to smoke can robustly be identified through their elevated concentrations of volatile phenols and phenolic glycosides serving as smoke markers, compared to concentrations typically found in non-smoke-exposed samples. Smoke-affected wines with high concentrations of volatile phenols and glycosides can have smoky flavours, but the relationship between concentrations of specific smoke markers in grapes and the intensity of smoky sensory attributes in the resulting wine has not been established. This study sought to determine whether volatile phenols and glycoside concentration in grapes and wine are suited to predict smoke flavour, to identify the key drivers of smoke flavour in both matrices. The study aimed to determine what concentrations of volatiles and glycosides in grapes impart an unacceptable smoke flavour in the resulting wine, to provide a guide for producers assessing suitability of smoke-exposed grapes for wine production. During vintage 2020, a total of 65 grape samples were collected from vineyards exposed to bushfire smoke, as well as unaffected vineyards. Chardonnay, Pinot Noir, and Shiraz grapes were harvested from vineyards in New South Wales, South Australia, and Victoria. Unoaked wines (50 kg scale) were produced under controlled conditions. The wines had a wide range of smoke flavour intensities rated by a trained sensory panel. Statistical models based on guaiacol, o-cresol, m-cresol, p-cresol, and some glycosides gave good predictions of smoke flavour intensity, with a slightly different optimal model for each cultivar. Subsequently, critical concentrations for quality defects were estimated to provide a guide for producers. A subset of smoke exposure markers in wine grapes affected by smoke from bushfires can be used to predict the degree of smoke flavour in wine. This information provides a first guide for assessing the risk of producing smoke tainted wine from smoke-exposed grapes

Enhancing location privacy for electric vehicles (at the right time)

An electric vehicle is a promising and futuristic automobile propelled by electric motor(s), using electrical energy stored in batteries or another energy storage device. Due to the need of battery recharging, the cars will be required to visit recharging infrastructure very frequently. This may disclose the users\u27 private information, such as their location, which may expose users\u27 privacy. In this paper, we provide mechanisms to enhance location privacy of electric vehicles at the right time, by proposing an anonymous payment system with privacy protection support. Our technique further allows traceability in the case where the cars are stolen. 2012 Springer-Verlag