While users claim to be concerned about privacy, often they do little to protect their privacy in... more While users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this "privacy paradox" is that when an individual shares her data, it is not just her privacy that is compromised; the privacy of other individuals with correlated data is also compromised. This information leakage encourages oversharing of data and significantly impacts the incentives of individuals in online platforms. In this extended abstract, we discuss the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We summarize work designing an incentive compatible mechanism that optimizes the worst-case tradeoff between bias and variance of the estimation subject to a budget constraint, where the worst-case is over the unknown correlation between costs and data. Additionally, we characterize the structure of the optimal mechanism in closed form and study monotonicity and non-mon...
GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017
We study a mechanism design problem of privacy- preserving data collection with privacy protectio... more We study a mechanism design problem of privacy- preserving data collection with privacy protection uncertainty. A data collector wants to collect enough data to perform a certain computation that benefits the individuals who contribute the data, with the possibility of individual privacy leakage. The data collector adopts a privacy-preserving mechanism by adding some random noise to the computation result, which reduces the accuracy of the computation. Individuals decide whether to contribute data based on the potential benefit and the possible privacy cost induced by the mechanism. Due to the intrinsic uncertainty involved in privacy protection, we model individuals' privacy-aware participation using the prospect theory, which more accurately models individuals' behavior under uncertainty than the traditional expected utility theory. We show that the data collector's utility maximization problem involves a polynomial of high and fractional order, which is difficult to s...
Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2018
We study a privacy-preserving data collection problem, by jointly considering data reporters'... more We study a privacy-preserving data collection problem, by jointly considering data reporters' data correlation and social relationship. A data collector gathers data from individuals to perform a certain analysis with a privacy-preserving mechanism. Due to data correlation, the data analysis based on the reported data can cause privacy leakage to other individuals (even if they do not report data). The data reporters will take such a privacy threat into account, owing to the social relationship among individuals. This motivates us to formulate a two-stage Stackelberg game: In Stage I, the data collector selects some individuals as data reporters and designs a privacy-preserving mechanism for a sum query analysis. In Stage II, the selected data reporters contribute their data with possible perturbations (through adding noise). By analyzing the data reporters' equilibrium decisions in Stage II, we show that given any fixed reporter set, only one data reporter with the most significant joint consideration of the social relationship and data correlation may add noise to his reported data. The rest of the data reporters will truthfully report their data. In Stage I, we derive the data collector's optimal privacy-preserving mechanism and propose an efficient algorithm to select the data reporters. We conclude that the data collector should jointly capture the impact of data correlation and social relation to ensure all data reporters truthfully reporting their data. We conduct extensive simulations based on random network and real-world social data to investigate the impact of data correlation and social network on the system. We find that the availability of social network information is more critical to the data collector compared with data correlation information.
IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, 2020
In an online social network, users exhibit personal information to enjoy social interaction. The ... more In an online social network, users exhibit personal information to enjoy social interaction. The social network provider (SNP) exploits users’ information for revenue generation through targeted advertising. The SNP can present ads to proper users efficiently. Therefore, an advertiser is more willing to pay for targeted advertising. However, the over-exploitation of users’ information would invade users’ privacy, which would negatively impact users’ social activeness. Motivated by this, we study the optimal privacy policy of the SNP with targeted advertising business. We characterize the privacy policy in terms of the fraction of users’ information that the provider should exploit, and formulate the interactions among users, advertiser, and SNP as a three-stage Stackelberg game. By carefully leveraging supermodularity property, we reveal from the equilibrium analysis that higher information exploitation will discourage users from exhibiting information, lowering the overall amount of exploited information and harming advertising revenue. We further characterize the optimal privacy policy based on the connection between users’ information levels and privacy policy. Numerical results reveal some useful insights that the optimal policy can well balance the users’ trade-off between social benefit and privacy loss.
We study a privacy-preserving data collection problem by considering individuals’ data correlatio... more We study a privacy-preserving data collection problem by considering individuals’ data correlation and social relationship. A data collector gathers data from some data reporters to perform certain analysis with a privacy-preserving mechanism. Due to the data correlation, the analysis will cause privacy leakage not only to the data reporters but also to those individuals who do not report data. Owing to the social relationship among them, the data reporters would consider the possibility of adding some random noise to the reported data to reduce the privacy leakage. The privacy loss of the individuals (both data reporters and non-reporters) depend on all the data reporters’ strategies, which naturally leads to a game theoretical analysis. A key result shows that the data reporters can be ordered based on their levels of joint considerations of social relationship and data correlation, and at the Nash Equilibrium of the game at most one data reporter with the most significant consideration may add noise to the reported data. We design an efficient algorithm for the data collector to construct the data reporter set, and derive the optimal privacy-preserving mechanism to ensure all the data reporters’ truthful reporting. We conduct extensive simulations with the Facebook social data to demonstrate some insights: It is optimal for the data collector to adopt a more conservative mechanism when the data correlation or the social relationship is stronger. Compared with the data correlation information, the social network information plays a more critical role in the data collector’s utility maximization problem.
We study a problem of privacy-preserving mechanism design. A data collector wants to obtain data ... more We study a problem of privacy-preserving mechanism design. A data collector wants to obtain data from individuals to perform some computations. To relieve the privacy threat
While users claim to be concerned about privacy, often they do little to protect their privacy in... more While users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this "privacy paradox" is that when an individual shares her data, it is not just her privacy that is compromised; the privacy of other individuals with correlated data is also compromised. This information leakage encourages oversharing of data and significantly impacts the incentives of individuals in online platforms. In this extended abstract, we discuss the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We summarize work designing an incentive compatible mechanism that optimizes the worst-case tradeoff between bias and variance of the estimation subject to a budget constraint, where the worst-case is over the unknown correlation between costs and data. Additionally, we characterize the structure of the optimal mechanism in closed form and study monotonicity and non-mon...
GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 2017
We study a mechanism design problem of privacy- preserving data collection with privacy protectio... more We study a mechanism design problem of privacy- preserving data collection with privacy protection uncertainty. A data collector wants to collect enough data to perform a certain computation that benefits the individuals who contribute the data, with the possibility of individual privacy leakage. The data collector adopts a privacy-preserving mechanism by adding some random noise to the computation result, which reduces the accuracy of the computation. Individuals decide whether to contribute data based on the potential benefit and the possible privacy cost induced by the mechanism. Due to the intrinsic uncertainty involved in privacy protection, we model individuals' privacy-aware participation using the prospect theory, which more accurately models individuals' behavior under uncertainty than the traditional expected utility theory. We show that the data collector's utility maximization problem involves a polynomial of high and fractional order, which is difficult to s...
Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2018
We study a privacy-preserving data collection problem, by jointly considering data reporters'... more We study a privacy-preserving data collection problem, by jointly considering data reporters' data correlation and social relationship. A data collector gathers data from individuals to perform a certain analysis with a privacy-preserving mechanism. Due to data correlation, the data analysis based on the reported data can cause privacy leakage to other individuals (even if they do not report data). The data reporters will take such a privacy threat into account, owing to the social relationship among individuals. This motivates us to formulate a two-stage Stackelberg game: In Stage I, the data collector selects some individuals as data reporters and designs a privacy-preserving mechanism for a sum query analysis. In Stage II, the selected data reporters contribute their data with possible perturbations (through adding noise). By analyzing the data reporters' equilibrium decisions in Stage II, we show that given any fixed reporter set, only one data reporter with the most significant joint consideration of the social relationship and data correlation may add noise to his reported data. The rest of the data reporters will truthfully report their data. In Stage I, we derive the data collector's optimal privacy-preserving mechanism and propose an efficient algorithm to select the data reporters. We conclude that the data collector should jointly capture the impact of data correlation and social relation to ensure all data reporters truthfully reporting their data. We conduct extensive simulations based on random network and real-world social data to investigate the impact of data correlation and social network on the system. We find that the availability of social network information is more critical to the data collector compared with data correlation information.
IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, 2020
In an online social network, users exhibit personal information to enjoy social interaction. The ... more In an online social network, users exhibit personal information to enjoy social interaction. The social network provider (SNP) exploits users’ information for revenue generation through targeted advertising. The SNP can present ads to proper users efficiently. Therefore, an advertiser is more willing to pay for targeted advertising. However, the over-exploitation of users’ information would invade users’ privacy, which would negatively impact users’ social activeness. Motivated by this, we study the optimal privacy policy of the SNP with targeted advertising business. We characterize the privacy policy in terms of the fraction of users’ information that the provider should exploit, and formulate the interactions among users, advertiser, and SNP as a three-stage Stackelberg game. By carefully leveraging supermodularity property, we reveal from the equilibrium analysis that higher information exploitation will discourage users from exhibiting information, lowering the overall amount of exploited information and harming advertising revenue. We further characterize the optimal privacy policy based on the connection between users’ information levels and privacy policy. Numerical results reveal some useful insights that the optimal policy can well balance the users’ trade-off between social benefit and privacy loss.
We study a privacy-preserving data collection problem by considering individuals’ data correlatio... more We study a privacy-preserving data collection problem by considering individuals’ data correlation and social relationship. A data collector gathers data from some data reporters to perform certain analysis with a privacy-preserving mechanism. Due to the data correlation, the analysis will cause privacy leakage not only to the data reporters but also to those individuals who do not report data. Owing to the social relationship among them, the data reporters would consider the possibility of adding some random noise to the reported data to reduce the privacy leakage. The privacy loss of the individuals (both data reporters and non-reporters) depend on all the data reporters’ strategies, which naturally leads to a game theoretical analysis. A key result shows that the data reporters can be ordered based on their levels of joint considerations of social relationship and data correlation, and at the Nash Equilibrium of the game at most one data reporter with the most significant consideration may add noise to the reported data. We design an efficient algorithm for the data collector to construct the data reporter set, and derive the optimal privacy-preserving mechanism to ensure all the data reporters’ truthful reporting. We conduct extensive simulations with the Facebook social data to demonstrate some insights: It is optimal for the data collector to adopt a more conservative mechanism when the data correlation or the social relationship is stronger. Compared with the data correlation information, the social network information plays a more critical role in the data collector’s utility maximization problem.
We study a problem of privacy-preserving mechanism design. A data collector wants to obtain data ... more We study a problem of privacy-preserving mechanism design. A data collector wants to obtain data from individuals to perform some computations. To relieve the privacy threat
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Papers by Guocheng Liao