Biblio

In this paper, we propose a new randomized response algorithm that can achieve differential-privacy and utility guarantees for consumer's behaviors, and process a batch of data at each time. Firstly, differing from traditional differential private approach-es, we add randomized response noise into the behavior signa-tures matrix to achieve an acceptable utility-privacy tradeoff. Secondly, a behavior signature modeling method based on sparse coding is proposed. After some lightweight trainings us-ing the energy consumption data, the dictionary will be associat-ed with the behavior characteristics of the electric appliances. At last, through the experimental results verification, we find that our Algorithm can preserve consumer's privacy without comprising utility.

Intrusion Detection Systems (IDSs) are crucial security mechanisms widely deployed for critical network protection. However, conventional IDSs become incompetent due to the rapid growth in network size and the sophistication of large scale attacks. To mitigate this problem, Collaborative IDSs (CIDSs) have been proposed in literature. In CIDSs, a number of IDSs exchange their intrusion alerts and other relevant data so as to achieve better intrusion detection performance. Nevertheless, the required information exchange may result in privacy leakage, especially when these IDSs belong to different self-interested organizations. In order to obtain a quantitative understanding of the fundamental tradeoff between the intrusion detection accuracy and the organizations' privacy, a repeated two-layer single-leader multi-follower game is proposed in this work. Based on our game-theoretic analysis, we are able to derive the expected behaviors of both the attacker and the IDSs and obtain the utility-privacy tradeoff curve. In addition, the existence of Nash equilibrium (NE) is proved and an asynchronous dynamic update algorithm is proposed to compute the optimal collaboration strategies of IDSs. Finally, simulation results are shown to validate the analysis.

Intrusion Detection Systems (IDSs) are crucial security mechanisms widely deployed for critical network protection. However, conventional IDSs become incompetent due to the rapid growth in network size and the sophistication of large scale attacks. To mitigate this problem, Collaborative IDSs (CIDSs) have been proposed in literature. In CIDSs, a number of IDSs exchange their intrusion alerts and other relevant data so as to achieve better intrusion detection performance. Nevertheless, the required information exchange may result in privacy leakage, especially when these IDSs belong to different self-interested organizations. In order to obtain a quantitative understanding of the fundamental tradeoff between the intrusion detection accuracy and the organizations' privacy, a repeated two-layer single-leader multi-follower game is proposed in this work. Based on our game-theoretic analysis, we are able to derive the expected behaviors of both the attacker and the IDSs and obtain the utility-privacy tradeoff curve. In addition, the existence of Nash equilibrium (NE) is proved and an asynchronous dynamic update algorithm is proposed to compute the optimal collaboration strategies of IDSs. Finally, simulation results are shown to validate the analysis.