A Game-Theoretic Study on Non-Monetary Incentives in Data Analytics Projects with Privacy Implications

The amount of personal information contributed by individuals to digital repositories such as social network sites has grown substantially. The existence of this data offers unprecedented opportunities for data analytics research in various domains of societal importance including medicine and public policy. The results of these analyses can be considered a public good which benefits data contributors as well as individuals who are not making their data available. At the same time, the release of personal information carries perceived and actual privacy risks to the contributors. Our research addresses this problem area. In our work, we study a game-theoretic model in which individuals take control over participation in data analytics projects in two ways: 1) individuals can contribute data at a self-chosen level of precision, and 2) individuals can decide whether they want to contribute at all (or not). From the analyst's perspective, we investigate to which degree the research analyst has flexibility to set requirements for data precision, so that individuals are still willing to contribute to the project, and the quality of the estimation improves. We study this tradeoff scenario for populations of homogeneous and heterogeneous individuals, and determine Nash equilibria that reflect the optimal level of participation and precision of contributions. We further prove that the analyst can substantially increase the accuracy of the analysis by imposing a lower bound on the precision of the data that users can reveal

AN ONLINE EXPERIMENT ON CONSUMERS\u27 SUSCEPTIBILITY TO FALL FOR POST-TRANSACTION MARKETING SCAMS

Post-transaction marketing offers are often designed to trick consumers into purchasing products they would not want. To increase the frequncy of transactions, retailers use strategies such as subscribing consumers by default to offers, camouflaging post-transaction offers as part of primary transactions, and being unclear about the terms of the offer. Further, sharing agreements for personal consumer data and payment credentials between first and third-party retailers violate consumers´ expectations about privacy and business conduct. Some of these tactics have been mitigated in the United States with the Restore Online Shoppers´ Confidence Act (ROSCA). In this paper, we report the results from an online post-transaction marketing experiment. We involve over 500 consumers in a purchasing episode (i.e., a functional mock-up music store) followed by a post-transaction marketing offer with low valu to consumers. In the experiment, we systematically vary important design characteristics on the offer page, and collect additional data through a post-experimental qustionnaire. We investigate which factors are most predictive of acceptance of the low-valu post-transaction offer. We find that ROSCA´s interventions are a step in the right direction and should be considered by European regulators, but do not go far enough

IntRepair: Informed Repairing of Integer Overflows

Integer overflows have threatened software applications for decades. Thus, in this paper, we propose a novel technique to provide automatic repairs of integer overflows in C source code. Our technique, based on static symbolic execution, fuses detection, repair generation and validation. This technique is implemented in a prototype named IntRepair. We applied IntRepair to 2,052C programs (approx. 1 million lines of code) contained in SAMATE's Juliet test suite and 50 synthesized programs that range up to 20KLOC. Our experimental results show that IntRepair is able to effectively detect integer overflows and successfully repair them, while only increasing the source code (LOC) and binary (Kb) size by around 1%, respectively. Further, we present the results of a user study with 30 participants which shows that IntRepair repairs are more than 10x efficient as compared to manually generated code repairsComment: Accepted for publication at the IEEE TSE journal. arXiv admin note: text overlap with arXiv:1710.0372