We present a novel technique for modulating the appearance frequency of a few tokens within a dataset for encoding an invisible watermark that can be used to protect ownership rights upon data. We develop optimal as well as fast heuristic algorithms for creating and verifying such watermarks. We also demonstrate the robustness of our technique against various attacks and derive analytical bounds for the false positive probability of erroneously “detecting” a watermark on a dataset that does not carry it. Our technique is applicable to both single dimensional and multidimensional datasets, is independent of token type, allows for a fine control of the introduced distortion, and can be used in a variety of use cases that involve buying and selling data in contemporary data marketplaces.

In light of the growing impact of disinformation on social, economic, and political landscapes, accurate and efficient identification methods are increasingly critical. This paper introduces HyperGraphDis, a novel approach for detecting disinformation on Twitter that employs a hypergraph-based representation to capture (i) the intricate social structures arising from retweet cascades, (ii) relational features among users, and (iii) semantic and topical nuances. Evaluated on four Twitter datasets -- focusing on the 2016 U.S. presidential election and the COVID-19 pandemic -- HyperGraphDis outperforms existing methods in both accuracy and computational efficiency, underscoring its effectiveness and scalability for tackling the challenges posed by disinformation dissemination. HyperGraphDis displays exceptional performance on a COVID-19-related dataset, achieving an impressive F1 score (weighted) of approximately 89.5%. This result represents a notable improvement of around 4% compared to the other state-of-the-art methods. Additionally, significant enhancements in computation time are observed for both model training and inference. In terms of model training, completion times are accelerated by a factor ranging from 2.3 to 7.6 compared to the second-best method across the four datasets. Similarly, during inference, computation times are 1.3 to 6.8 times faster than the state-of-the-art.

A large number of Data Marketplaces (DMs) have appeared in the last few years to help owners monetize their data, and data buyers optimize their marketing campaigns, train their ML models, and facilitate other data-driven decision processes. In this paper, we present a first of its kind measurement study of the growing DM ecosystem, focused on understanding which features of data are actually driving their prices in the market. We show that data products listed in commercial DMs may cost from few to hundreds of thousands of US dollars. We analyze the prices of different categories of data and show that products about telecommunications, manufacturing, automotive, and gaming command the highest prices. We also develop classifiers for comparing data products across different DMs, as well as a regression analysis for revealing features that correlate with data product prices of specific categories, such as update rate or history for financial data, and volume and geographical scope for marketing data.

Spatio-temporal information is used for driving a plethora of intelligent transportation, smart-city and crowd-sensing applications. Data is now a valuable production factor and data marketplaces have appeared to help individuals and enterprises bring it to market and the ever-growing demand. Such marketplaces are able to combine data from different sources to meet the requirements of different applications. In this paper we study the problem of estimating the relative value of spatio-temporal datasets combined in marketplaces for predicting transportation demand and travel time in metropolitan areas. Using large datasets of taxi rides from Chicago, Porto and New York we show that simplistic but popular approaches for estimating the relative value of data, such as splitting it equally among the data sources, more complex ones based on volume or the "leave-one-out" heuristic, are inaccurate. Instead, more complex notions of value from economics and game-theory, such as the Shapley value, need to be employed if one wishes to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms. This does not seem to be a coincidental observation related to a particular use case but rather a general trend across different use cases with different objective functions.

In this paper we propose a method for using mobile network data to detect potential COVID-19 hospitalizations and derive corresponding epidemic risk maps. We apply our methods to a dataset from more than 2 million cellphones, collected by a mobile network provider located in London, UK. The approach yields a 98.6% agreement with released public records of patients admitted to NHS hospitals. Analyzing the mobility pattern of these individuals prior to their potential hospitalization, we present a series of risk maps. Compared with census-based maps, our risk maps indicate that the areas of highest risk are not necessarily the most densely populated ones and may change from day to day. Finally, we observe that hospitalized individuals tended to have a higher average mobility than non-hospitalized ones.

Ride-sharing systems have the potential to match travelers with similar itineraries and time schedules, and to bring significant benefits to individual users and the city as a whole. However, this is a challenging task, since users' requests are not known in advance and they become available a few minutes before departure. In this paper, we design an online ride sharing system, where drivers and passengers send their requests for a ride in advance, possibly on a short notice. Our design is efficient and optimal. This is achieved by dividing the system into two components: the constraint satisfier and the matching module. The constraint satisfier takes as input the spatio-temporal constraints of drivers and passengers and provides feasible (driver, passenger) pairs in real time, and the matching module takes as input the feasible pairs and provides a maximum cardinality matching of drivers and passengers. Our preliminary evaluation shows that the constraint satisfier can resolve the most expensive queries (matching of passenger to en-route drivers) in 2 seconds (on average), while the matching module can achieve a matching ratio of 78% when the offline upper-bound is 80%.

Distributing long-tail content is an inherently difficult task due to the low amortization of bandwidth transfer costs as such content has limited number of views. Two recent trends are making this problem harder. First, the increasing popularity of user-generated content (UGC) and online social networks (OSNs) create and reinforce such popularity distributions. Second, the recent trend of geo-replicating content across multiple PoPs spread around the world, done for improving quality of experience (QoE) for users and for redundancy reasons, can lead to unnecessary bandwidth costs. We build TailGate, a system that exploits social relationships, regularities in read access patterns, and time-zone differences to efficiently and selectively distribute long-tail content across PoPs. We evaluate TailGate using large traces from an OSN and show that it can decrease WAN bandwidth costs by as much as 80% as well as reduce latency, improving QoE. We deploy TailGate on PlanetLab and show that even in the case when imprecise social information is available, TailGate can still decrease the latency for accessing long-tail YouTube videos by a factor of 2.