enrichment for the data and so the data from data sf, comes with latin long, associated with it and latitude and longitude coordinate and so for a contributor, you can see where the contribution was made and that is useful, but we might want to know from a lat long, what state was that lat long from? what sf district was it from. what california county did the money come from? and you use that, you know, enrichment, to show further correlations between things, do we find that certain kinds of activities are associated with certain geographic regions. and you know, to answer that kind of question, requires, some sort of processing. and processing a data set for that geo enrichment can take, hours to days to find the positions within these geographic regions. and so, there is a lot of processing that needs to be done, and there is a lot of sort of intermediate data that needs to be stored, and going forward we are going to be looking at more of that, and seeing if we can make more out of sort of this foundation, that has been provided to us. and so, yeah, we are very excited and one o

we are not dealing with the collection of data. this gets to the point of use of data. we have two high-level categories that deal with managing this. ,ust as technology has expanded it is also significantly expanded the privacy protection that is available at the agencies. ago,u imagine 50 years paper.s people of maybe it was locked in a desk or. maybe it was in a dusty basement archive. limited tracking of where the file was. who knows? anybody who had access couldn't see what ever was in it. you just needed to rifle through it and you could see anything. oversight into how it would be used would be irrelevant. deletion would be a shredder. more precise deletion would be a black magic marker redirecting points of information. technology allows us to do a lot more management of data. is the access control point. you can build access controls to manage data very precisely. you can do it in a more nuanced way. you don't have to make choices between access or non-access. there are lots of options. infiniteve you an variety and how to manage data and what they can do with

here's the kind of data we gary gather here's other data about you. that's no so the bad if you're honest. you can have abbreviated knew nutrition facts and maybe there's a longer version below. is a mentioned, credit cards and some financial statements you -- some of mutual fund statements have simplified what they do with fees. i don't -- i think that honesty should be rewarded by customers itch think there's a lot of customers who are frustrated by the deliberate obfuscation of what the companies are doing, and they're crying out for something simpler and your study you just mentioned is an example of that. please, over here. >> block planning to write something understandable, i think it's the way to make you just skip to the end and say, i agree, and then whatever you need to do, whether it's to buy something on amazon or whatever, then you're just agreeing to -- first of all you know that you will not understand it, unless you are a ph.d, and then i guess the -- exactly not to make it understandable, so that people just skip and go to the end. o

this data is 2013. none of the target home depot, linked inn, none of that information, none of those attacks are included here so with that i close my remarks and look forward to answering questions. thank you. >> thank you. we'll now go through a round of questioning and board members as well will be subject to the time limits here. i think ef-20 minutes and then each board member will have five minutes and then still the possibility of questions from member members of the audience. i wanted to build my first question off of the point that i think hadi was making at the end there which is that there seems to be this inexorable trend towards more sophisticated devices, collecting, generating, sharing, emitting autonomously, automatically disclosing more and more information and i think i'll go to professor anton first and maybe come back to hadi with this. but looking that the phenomenon and the seaming inexrabbit of it, the seaming inevitability of it, first on the technology design side, on the techn

so, do we have a motion to continue this item until we get the data? >> i move to continue. >> i second. >> roll call? >> all right. commissioner, we have a motion to continue the item until the data is gathered. so, president adams? >> aye.

data. building a technical means based on principal scientific methodologies to support the identification of civil liberties and privacy risks can help us protect it in a fluid world of big data. success is dependent upon input from a variety of disciplines, ranging from technologists, social scientist, toeshs and computer scientists to name a few. he would would welcome the opportunity to discuss this in more detail and greater technical depth at a later date. i thank you for the opportunity and i'm happy to answer what i'm sure are a couple of questions. >> thank you all for your opening remarks. becky, i want to stick with you for a second. when we go and meet with you and when we talk to you, there is frequently someone from the general council's office, from the compliance office, someone from your office. what are you doing that is different than the general council's office and a compliance shop. >> that's a great question. the civil liberties and privacy office is the focus point.

so, do we have a motion to continue this item until we get the data? >> i move to continue. >> i second. >> roll call? >> all right. commissioner, we have a motion to continue the item until the data is gathered. so, president adams? >> aye. >> kathleen dooley? >> aye. >> commissioner dwight? >> aye. >> commissioner ortiz-cartagena? >> yes. >> commissioner tour-sarkissian? >> yes. >> commissioner white? >> yes. >> and commissioner irene yee riley? >> yes. >> all right, so, commissioner, we have 7 votes to continue to hear the item once the advisory board has put together its recommendation. >> great. thank you. next item. >> next item is discussion and possible action to make recommendations to the board of supervisors on bos file no. 141006 [administrative code - legacy business registry, rebate program, and establishing fees]. ordinance amending the administrative code to direct the small business he commission to establish a legacy business registry, authorize an administrative fee for the registry not to exceed $50, and, for the next five years, pr

data from a.l.p.r. devices, including photos, a time stamp and location is retaineded in thousands of databases across the country and shared by various agencies and sold to private companies, with little or no regulation. he says it's unconstitutional. >> if you don't have a probable cause against as citizen or individual, why are you keeping data. there's no reason to do it. >> use of al pr has interaffed. >> jennifer limp, a lawyer with the electronic frontier foundation says with a.l.p.r. affordable. >> it has a position to collect data on a scale we have not soon. in los angeles, the l.a.p.d. has 250 squad cars equipped with cameras, and 35 satisfactionry cameras. each records up to 1800 plates per minute with a capacity to collect data, they are very, very large. >> large and according to limp pay to invasive. >> if a licence plate camera pecks up the licence plate many team during the book, it pinpoints the location, charting the pathway through loif. that reveals spsive information, telling who

whether we call it big data or just high volume data. but the notion of a sort of fips like approach particularly with the focus on the individual when the broader issues are frankly societal. maybe the impact on the civil liberties. not of one person but everybody. i don't know that the fips help focus on the way and frankly the fips led to some silly results. i would just mention i've been surprised by example by the department of homeland security privacy impact assessment on border searches of electronic devices. which focus a lot on notice adds privacy protection. at the point that your device has been seized from you and its contents cop ooed, it is difficult to think that notice is meaningful protection. it may be necessary but whether it's protection or not, i think it's not. second point, one of the things we are seeing emerging in the debate in the private sector and we see this especially in europe and the context of discussing the general data protection regulation there is greater focus on risk management or risk assessment

commander data knows precisely what he's doing. forgive me, mr. data.to working with nonliving devices that... forgive me again. your service record says that you are alive. i must accept that. engineering. geordi: engineering. lieutenant, are all your systems functioning? yes, captain. wherever we are, it seems to have no effect on engineering. thank you, lieutenant. data. difficult to make a judgment, sir, based on the absence of information. speculate. this void has a total lack of dimension. therefore, by any accepted standard, it does not exist, yet being within it denies that conclusion. might we have moved into another dimension? could a lack of dimension be another dimension in itself? that's an interesting question. for a later discussion. [ sighs ] number one, i think we should get on with our mission. starfleet can send a science vessel back to investigate this further. move us back out of here. wesley, reverse our direction. set a course for the cornelian star system, impulse power. aye, sir. reversing direction. course slated. engage. our e

data. but there is a program that has been running in las vegas in lapd, we may not necessarily still be able to identify specific criminals, but our predictive modelling systems have been at work. they're able to make a reasonably good prediction about where the criminal activities are more likely. it is not precisely the question that you're asking, but i can assure that it is just becoming better. i can assure that any service provider that has the amount of data that we are generating and it's becoming more and more and more generated is just a honing and fine tuning and polishing their models. whether it's going to be applicable to anti-terrorism methods, i don't know. i think all of these models are heavily data driven. so one would need a lot of data. but to the point that these models, these predictive modelling are able to predict things that may relate indirectly to terrorism or criminal activities, the systems are suggesting that we are going that way. >> other thoughts on that q

this data is 2013. none of the target home depot, linked inn, none of that information, none of those attacks are included here so with that i close my remarks and look forward to answering questions. thank you. >> thank you. we'll now go through a round of questioning and board members as well will be subject to the time limits here. i think ef-20 minutes and then each board member will have five minutes and then still the possibility of questions from members of the audience. i wanted to build my first question off of the point that i think hadi was making at the end there which is that there seems to be this inexorable trend towards more sophisticated devices, collecting, generating, sharing, emitting autonomously, automatically disclosing more and more information and i think i'll go to professor anton first and maybe come back to hadi with this. but looking that the phenomenon and the seaming inexrabbit of it, the seaming inevitability of it, first on the technology design side, on the technology d

data, electoral data, and crunch that on the backend. once we do that, we are able to look at things like, what is the profile of this individual legislator? how has he or she voted in the last 20 years? or happens to be a bill that relates to something they have looked at before. can we go back into the data and extrapolate into the future, and come with those outcomes? like this is done via an algorithm you developed? how old are you? >> i am 22. >> how did you get interested in all of this? likes my own career has sort of taken a lot of twists and turns in the last couple of years. in they started off obama campaign in 2008, working in the field and then moving to the data side, initially looking at some of the more demographic type issues, those types of things, but moving more into issue-based things. after the campaign ended, i was thinking of what i could do. i was 17 at the time. and ended up really deciding to throw my hat into the race for a board of education seat that came up in maryland. and to my surprise, i won, the stone

data. you know, i'm not sure we gould so far to say that a warrant is required for every single case for every single data type. we certainly need to update the rules so that there is appropriate judicial review of surveillance programs and specific requests that we get for data. >> in terms of third-party doctrine, would you then essentially not have it be an absolute exception to the fourth amendment but essentially where would grow with it to provide some protection but not necessarily a full warrant protection? >> yeah. the laws that we deal with in the law enforcement context provide a sliding scale in effect. provide some reasonable oversight in protection, something below warrant and probable cause. and we've taken the position that's appropriate for some types of subscriber data, et cetera. >> thanks. heidi, you talked about -- i want to put this in the context of how much information should be collected, you talked about enforceable rules for collection, but you also said that coll

they help gather data, analyze data, and sure data with the government. i think all these things create various problems that we need to address. and so i think if we can keep our eye on the problems and stop looking elsewhere and just look at the problems, and address those problems, wherever they may happen, i think that's the best approach. >> okay. so the panel discusses what they think their tesla, i today ask what that was, what technology should be built in -- i know you have covered a great deal in this report. if you could just give us kind of one piece or summary on it, that would be kbret. >> in a sense, the question is asking me to sum up a whole area of knowledge in a second. which i won't try do. i simply say that as with cars, as with the tesla, some sort of high end car, you should think in terms of which technologies are available and reasonably prak tig al to use to minimize or control or limit the risk of a certain information practice. and then ask that those be there. that you should ask that an entity that wants to collect and use th

users of the data could be charged to use the data and they could pay for that data with dollars or in contributions of their own data into the system. in terms of it is already being done, those are microcosmic by comparison, not at systematic testing, i remember 23, an mri, no one is testing those and so forth and fluids and as i said, this is a mother ship in which other concepts can dock and trade data and that is why after being funded, managed in some regard by the federal government. >> i was originally going to comment on dr. hanlon's comments but there's a lot of rich discussion. one additional would make that has already been said more broadly is whether it is a big longitudinal study or a smaller study taking advantage of existing data what is critical is we know which questions need to be asked and we designed the study in such a way that would get meaningful answers to tell us what we need to know and that is so important and there's a lot going on in the area of clinical trial designs, trying to ask and answer questions in ways that require smaller numbers and shorter tri

this is what data minding is all about and data analytics. this then leads to the revelation of someone that they might not have expected or wanted when they gave out little pieces of information here and there. and i think this causes a problem. it disrupts people's privacy expectations, it can leads to knowledge of information that people don't want exposed or that society might not want exposed. they say, well, if the information were all different facts that were gather the from public information, it's all privacy. i don't think that's true. i think we want to look at what the problems are, and if we look at a problem, there is a problem here. the other problem is exclusion, which is the fact that people lack an ability in a lot of cases to have any say in how that information might be used against them. any write to correct that information or to make sure that it's accurate. and i think that's a key component of a lot of privacy laws is a right for people to make sure. we link individual data to a particular individual. by identifying

the data more separately, and preprocessing or minimizing the data first. and there's a growing array of technical methods that can do this. nfortunately, this becomes a technical problem. so the key principle here is just simply to insist that that technical work be done to try to architect the system to mum of and hold a min data. >> who should do that? the government or private industry? >> in my view, if say a -- if a government agency wants to argue that they have a need to collect and use certain data there should be some onus on them to justify the technical practices they're using to justify the amount of data collected, the way they're organizing it and so on, that those who would argue for a collection and use of data should be prepared to discuss these issues and offer a technical justification. when it comes to private parties, that's a more complicated discussion. think that the best practice in industry ought to be to do that as well. although obviously the legal and market mechanisms that drive that relationship are very different. >> my last

the data with us. the database includes both removals and a certain number of informal returns. our report and my presentation today focus exclusively on formal removals. we're taking the returns out of the conversation. we also used dhs data to model the nonremovals that don't appear in the data set that are in the dhs data set because they were also removed. that's basically cpv removals to mexico and canada, nonjudicial removals to mexico and canada. and then throughout the report and my presentation the data measure removal events, not unique individuals so, what that means is some people are deported more than wasn't and they appear more than one time in the data set so it's a certain bias in the data toward these sort of repeat customers. all together, 3.7 million people have been removed between 2003 and 2013, so that's the universe that we're looking at over that 11-year period. just sort of a basic profile of who's been deported during that period, fornally removed during that period, 91% of r

this is what data mining was all about and data analytics.the whole becomes greater than the parts. it starts to create a mosaic, a portrait of somebody. this then leads to the revelation of information that someone might not have asked that did or wanted when they gave out little pieces of information here and there and i think this causes a problem and disrupt people's privacy expectations. it can lead to knowledge of information that people don't want to expose her that society might not want exposed. i think we need to address that problem and often times conceptions of privacy will ignore aggregation because the information on what different gathered from public information here there's no privacy. i don't think that's true. we really want to look at what the problems are. another aspect of the problem i call solution, which is the fact that people lack an ability in a lot of cases to have any say in how the information might be used against them. any right to correct that information or make sure it's accurate. that is a key component o

all of these models are heavily data-driven. so a lot of data.but to the point that these models, predictive modeling are able to predict the things that they were laid indirectly to terrorism or criminal activity, the systems are are saying we are going that way. >> other thoughts on the question? there's a system in chicago the chicago police department has deployed which both has been touted and criticized, but it does somewhat the neighborhood or block level predictive, predictions as to criminal activity, as well as understanding individual levels identify people who may be either victims of crimes or perpetrators of crimes. again, both touted and highly criticized. any thoughts or comments speak with one quick one which is a few predictability crime on corner x., you watch corner x. like a hawk, you see every crime and, therefore, drop in over represent example of crimes at corner x. >> this is certainly not my area of expertise. however, predicted his being able to reconstruct after the fact. so can we use these things when something's hap

, other data.currently we have health care policy fellowship, we have organizations, employer organizations such as the pacific business group on health, consumer advocacy all advocating apcd on cost containment. in that avenue, the comprehensive system on the market remised s -- released to 13 vendors. i'm not going to read all of these quotes on here about the suffice it to say it's been long time coming which dates back to 2009 on reporting and information. we have been able to view our data on fore costing and trends and simply improve our care and reduce our cost by incorporating that claims district -- data. so long time mission. and to that end we've also had delays and the intent and delays have been reported over the years to this board. in september of 2012, we had started to pick up traction and that was related to committee on health and wellness on accountability and transparency we had a few milestones come out of it. we had released a joint statement in support of accountability and

data. data, wait! data! wait! data! moriarty: why does it frighten you, holmes?! data.hat's going on? computer, exit! computer, execute complete shutdown of the holodeck. access denied. explain. override protocol has been initiated. it's still running. the program didn't shut down. we must see the captain. data, wait, what is it? what's on that paper? and why can't we shut down the holodeck? data! this. this is impossible. how can a character from 1890s london draw a picture of the enterprise? who's got control of the computer? he does, moriarty. that is impossible. i don't understand. nor do i. data, wait, what about the doctor? is she all right in there? no. she is in grave danger. just want to say, i bundled home and auto with state farm, saved 760 bucks. love this guy. so sorry. okay, does it bother anybody else that the mime is talking? frrreeeeaky! [ male announcer ] savings worth talking about. state farm. frrreeeeaky! you know how fast you were going? about 55. where you headed at such an appropriate speed? across the country to enhance the nation's most reliabl

you can find financial data, marketing data, sale data on the fly.e really want right sized information. they don't want weeks and weeks. >> who are some of your customers? >> mid to large enterprises. we are focused on scale making it easy but at the same time, keeping it scalable. when you talk about accessibility, that's great. on the other side of the coin is security and scalability. they have some large volumes of data and a large number of users. being able to provide these but meeting scalability and manageability needs is sort of a key focus of thoughtspot. >> so much has been made about the potential of big data that we have yet to see it become truly usable. it's difficult to go through all of that information. has big data been overhyped or have we not figured it out yet? >> we are learning how to use it. thoughtspot is a classic way of turning data into insights. you don't actually need to know the full query. it suggests things just like the google search box but on enterprise data and very large amounts of data. applications like thoug

from most data we have. summarize, survey show as many as two out of five drivers report having fallen asleep while driving at some point in their lives. 11% of drivers admit doing that in the past year. the past doing that in month that is consistent across multiple studies by different organizations over the span of a decade. official statistics estimate 1-2% of crashes involve rousing -- the began likely study with the best data and research methods consistently show a much higher prevalence of drowsy driving. on the low end. on the high-end come as many as driving asng drowsy the cause or can should bidding factor. the data tends to suggest the prevalence is on the high-end of that range. thank you. >> thank you. in your presentation, you are very clear that there are severe and safety consequences. could tellng if you us in simple, general terms what is affect in combating fatigue. >> the primary countermeasure is adequate sleep. healthy sleep. it was your other speakers talk about what healthy and unhe

first, collect data, second, merge data item, and, third, analyze the data to infer facts about people. the first stage is collection. in our daily lives we disclose information directly to people and organizations. but even when we're not disclosing information explicitly, more and more of what we do online and off is record it. online services often attach unique identifiers to these recordings which are used to link them up again later. the second stage of the pipeline merges the data. if two data files can be determined to correspond to the same person, for example, because they both contain the same unique identifier, then those files can be merged. and merging can create an avalanche effect, because merged files convey more precise information act identity and behavior and that precision in turn allows further merging. one file might contained detailed information act behavior, and another might contain precise identity information. merging those files links behavior and identity together. the third stage of the pipeline uses big data methods such as predictive analytics to infer

flowing into data centers.so it seems to me we have two things going on at once which is not unusual. somebody referred to the modern era, the era of the internet of things and big data and you ubiquitous data flows as the golden age of surveillance. and it seems to me that both trends will always be there. more and more information available both to the private sector and possibly to the government and increasing pervasiveness or at least increasing diffusion if not comprehensively diffusion of encryption. comments on that as a premise, first of all, the premise of my question. am i right? and then secondly, where does that lead the government, and would you agree with my assumption that there will still be huge amounts of information available both to the private sector for its purposes as well as to the government? i guess let's go right down the row. professor anton? >> so i believe that there will still be a lot of data that's available to the government. when i say that i really support end description by

if you count data sets, we are in the tens of thousands of data sets you can access now. you can download them. you can connect to them increasingly. you can run a new application. if you asked yourself all the places where we collect information -- can i get what i want? not yet, but president obama has made it clear this is a commitment he has made. congress with the most recent data act has said financial transaction status shall be made more open. and, piecemeal parts -- the medicare database, slowly opening up. other data sets are coming online. here is the point -- we would no longer have the debate of whether we should or should not open. it will be the pace of which we find new assets and bring them to bear. it opens up a new conversation. what new data should we be collecting that we have not been? how well performing is our workforce system? is there a skills gap? why's it so hard for veterans to find jobs who are heroes and so many employers are saying i want to hire a veteran yet the unemployment rate is much higher amongst the younger cohort. it will be infor

could use their data. if i dedicate midlife to studying these complex issues, i would want to make sure it could be used. the other claim is that this bill will make public personal health care information which could be against the law. this legislation makes clear that nothing in this bill requires the public dissemination of information, the disclosure of which is prohibited by law. the data sets must only be made available in a manner that is sufficient for independent analysis and substantial reproduction of research results. numerous congressional hearings and testimony from experts made it clear that this information can easily be made anonymous. this is how data sets are presented to the peer review community and published for journals already. this is the transparency the american people deserve. they should no longer be held guilty from data they can't see or black box economic analysis deem prod pry tear. that's why i urge my colleagues to support this bill and i yield back the balance of my tim

i basically just went on to the data s.f. and put in specific terms looking 30 years ago or older and then also certain class codes [speaker not understood] felt would be best that fall under the definitions of it and then just compile it had into an excel spreadsheet. you only have one class code. i did a couple more and i hope to continue to work with that project because i really think it's a great project and would like to provide any help that i can. >> thank you. >> so, thank you, stephanie, and monique. so, you're welcome to stay or you're welcome to go back downstairs if you like. all right. also, so, to move on, so, the a-d-a subsidized assessment program we are now going to expand it city-wide. and it will be available as long as there are funds annually because of the dollar registration that's added to the annual business registration. we have approximately $50,000 to work with. so, that could add 40, around 40 kass p, subsidized casp inspections. it was opened up to targeted certain neighborhood programs. now wit

that data -- those statistics are based on data that is collected annually and is presently available through 012. the most recent data from 2012 shows that 1.6% of drivers involved in fatal crashes in 2012 were believed to be drowsy or fatigued at the time of the crash. >> what would you say is the general trend for the drowsy driving data? is there a trend and, if so, which direction is it heading? >> in official statistics, there does not seem to be much of a general trend. if anything, the proportions in the official statistics may have decreased slightly. as i talk about in my presentation, i believe that is a vast underestimate. i would not look to the data published in those sources for evidence of a trend or lack thereof. in the study that i conducted that looks at eta from 1990 99 - data from 1999-2008, in that data, there was not evidence of an increasing or decreasing trend in the proportion of the crashes that involved drowsiness. i would note that, in recent years, the raw number of crashes and deaths nationwide has decreased and that is a good thing. i do not believe the

census data from 2011 shows that the average woman still earns 77 cents for every dollar earned by men. the pay gap is even greater for african-american women and latina women f. you're an african-american woman, you earn 64 cents on the dollar and if you're a latina, you earn 56 cents on the dollar. women comprise more than 50% of the labor force in the united states and the consequence of wage discrimination are not only wrong, but it means beyond the impact on women that it actually impacts all of us. in san francisco where we are experiencing an affordability crisis, it means that women and their families are having a harder time paying rent, securing quality health care, paying for child care, and saving for a rainy day or retirement. eliminating wage discrimination is not only a matter of fairness. it will have real material impacts on women, their families, and society as a whole. my legislation will take a step and it's only a step in ensuring that taxpayer dollars that we as a city spend are actually spent on contractors that pay women equally for the work they do. last year t

and the benchmark data. the characteristics and i cannot stress this enough is that all data will be identified. we will not be seeing any employee level data of identifying any employee at all. it will be confidential. and the well being assessment data annually. as far as the robust analytics we'll be able to do our analysis on the wellness of the diseases and programs and certainly not only cost and utilization importance to us but the quality of care our members receive is very important to us and we'll be able to use database guidelines for those gaps of care and we'll be using our ad hoc analyses for audits and clinical procedures for ncqa and we'll be able to integrate our plans to provide those integrated dash boards. at this point i would like to turn the presentation to my colleague david flores. >> i get the short part of this presentation and i'm very very pleased about that. president fraser, commissioners, we've given you the background for our clearance data base and now we are at the nuts and

the fatality system, the data there is only based on data from reports completed in the field by police officers and they in most cases i don't believe they make a determination between a distinction between whether a driver was known to be taintive or whether they simply couldn't tell whether the driver was drousey or not. there is actly an indication in whether the driers' condition at the time of the crash was unknown. however the proportion in which they indicate the driver's condition was unknown is imapplauseably low even among only one which driver was involved, or where the driver was deceased at the time. they only report that the driver's condition is unknown. in more than 60% of the cases they report no contributing factors apparent and i'm not sure how one could arrive that determination when the only person involved is already deceased at the time they arrived. >> i want to comment on the notion of a prevalence changing. there is no question that the introduction of rumble strips and airbags may have made it safer or less likely to be lethal for somebody who drives drowsy.

off the back of weak european pmi data. as you can see, italy's laggard, the ftse 100 slightly outperforming, only down 0.7%. >>> that seems to be weighing on u.s. markets which right now are trading lower in premarket trade after stocks boosted motdest losses on the back of the fomc meeting minutes. >>> lbrands third quarter profits rose beating forecasts and the group raised its full year outlook. the parents of victoria's secret and bath & body works has been a relatively bright spot in the industry. l brands raised guidance earlier this month. trading thus far in frankfurt, flat for the day. >> and is, of course, americans are getting ready to shop till they drop this holiday season. the national retail federation estimates 140 million people are likely to hit stores or shop joan line over the thanksgiving weekend. specifically, 29% say they would shop and about a third are in the waiting category to see if deals are worth it before heading out. to talk about retail, let's bring in stacey. great to have you in studio. i

we don't hold data forever. we also are required to ensure that we maintain protection of the data from the moment we collected to the moment we purchase it so we still sell data. we have to remain strict control of information we have been granted authority to collect collect. when we are doing bulk collection overseas for example when we become aware of anything specifically tied to a u.s. person where to stop what we are doing and making a decision in our own mind okay as their new legal connection and is there a nation-state or group to get permission or do we just stopped collecting? we have to make that decision and we have to make a legal case if we want to continue. so there is the legal framework to what we do. there's a series of protections and oversight to what we do both external to the organization and multiple branches of our government. there's also a series of controls in place within the organization. it's one reason why i would say look, you can certainly disagree about the legalities in terms

this legislation is groundbreaking because it provides the human rights commission the data it needs to proactively investigate businesses without having to rely on individual worker complaints and therefore ensure equal pay at hundreds of businesses that contract with the city and county of san francisco. the legislation is also unique in that it contains consequences he for contractors that are violating equal pay laws ~. if after an investigation the human rights commission believes that discrimination has occurred, the contractor can be required to pay monetary penalties or risk losing its contract with the city. we are the first government entity to attach consequences to failure to comply with equal pay laws. i want to make it clear that the passage of this legislation, the discussion on what data to collect and how to do so is only the beginning once this legislation is passed. before any provision of the ordinance related to the equal pay report goes into effect, the equal pay advisory board that is created by this law must be fully in panel and it would take up to at least t

in any city in california, and then they also took the state's data and included it as well. and so you can actually tap into all of the data state wide and so you could build something like this now and again, this is only, it has developed in the past week, so a lot of stuff is moving at the moment. >> thank you. >> any other questions from the commissioners? >> well, thank you very much for the presentation. we appreciate it, and thank you for your hard volunteer work. it is really great, and i think that the public is going to greatly benefit from it. [ applause ] public comment, on agenda item three? >> i too would like to compliment these volunteers, the website looks terrific. i think journalists larry bush at city report.com would absolutely love this new website. so, keep on encouraging them to work with mr. massey. >> david, pilpal speaks an individual, three comments, it would be nice if the group could explain the light blue and the dark blue, and the difference between the blue and the red and i just saw it quickly. >> and i think that there is an explanation, an

data collected and how long it is retained, choice of a collection and use of data, strong securityto ensure this data is protect it and accountability to ensure we are living up to our commitments. the standards are not just a rule, but we hope our follow. the processes we use to operate our business. for example, they are built into the tools used in our software development lifecycle and checkpoints that prevented product service from shipping without a privacy sign off. some have taken what is often referred to as privacy by design approach to how we operate the company and how we develop and run our services. this is supported by mature privacy programs including dedicated personnel as privacy expertise to sit them both centralized roles and throughout the business. the program includes management response and escalation processes. further, we've developed and deployed comprehensive role-based training for engineers, sales and marketing personnel as well as h.r., customer service and other roles that touch and handle personal data in our program to the executive level accountab

i won't be talking so much about big data for spooky uses but data for use by you and me. all right. so i'll be talking about public information and access to public dock uument documents. focusing on a few other ancillary matters and one of my copresenters will be dealing with the public information side of this. so i want to -- there have been a number of cases decided this year. there are too many to summarize in a talk to there's a rather extensive summary of many of the cases that were decided this year and decided in this era. i will concentrate on a few cases. let me start with the definition of agency records because only agency records are subject to foya. this year there have been two notable cases on that subject. course of action versus national archives and records administration and story county. story county which is on page ten of the materials, that involved records a local sheriff generated as a board member of the first responders network authority or first net. the u.s. district court in iowa held that the records were federal records and enjoined count