Full counting statistics of information content

We review connections between the cumulant generating function of full counting statistics of particle number and the R\'enyi entanglement entropy. We calculate these quantities based on the fermionic and bosonic path-integral defined on multiple Keldysh contours. We relate the R\'enyi entropy with the information generating function, from which the probability distribution function of self-information is obtained in the nonequilibrium steady state. By exploiting the distribution, we analyze the information content carried by a single bosonic particle through a narrow-band quantum communication channel. The ratio of the self-information content to the number of bosons fluctuates. For a small boson occupation number, the average and the fluctuation of the ratio are enhanced.Comment: 16 pages, 5 figure

The Use of Self-Information and Environment in Counseling Process

Counseling as one of the forms and techniques of assistance provided by schools in the context of implementing overall guidance, this is given to help students in their efforts to solve all problems. The availability of this information makes it easy for students to obtain the information needed for example to implement a decision, want to know about something both in terms of themselves and their environment. The use of self and environmental information in the counseling process occurs in the creation of rapport, in exploring, discovering the real problem, exploring and studying alternative problem solving, decision making, implementation of decisions taken and in evaluation and follow-up. Therefore the information used must meet the requirements: that is, careful and not prejudiced, up to date, comprehensive and available information sources. In addition to the above conditions, the principles of time saving, non-authoritarian and feedback are also taken into account, so that this can cause self and environmental information in the counseling process to be very important material for both the counselor and the client

Privacy Against Statistical Inference

We propose a general statistical inference framework to capture the privacy threat incurred by a user that releases data to a passive but curious adversary, given utility constraints. We show that applying this general framework to the setting where the adversary uses the self-information cost function naturally leads to a non-asymptotic information-theoretic approach for characterizing the best achievable privacy subject to utility constraints. Based on these results we introduce two privacy metrics, namely average information leakage and maximum information leakage. We prove that under both metrics the resulting design problem of finding the optimal mapping from the user's data to a privacy-preserving output can be cast as a modified rate-distortion problem which, in turn, can be formulated as a convex program. Finally, we compare our framework with differential privacy.Comment: Allerton 2012, 8 page